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<title>Tunnels of attention: reconsidering issue competition</title>
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            // Allow the caller to control whether this is dismissed
            // when it is clicked (e.g. sidebar navigation supports
            // opening and closing the nav tree, so don't dismiss on click)
            const clickEl = placeholderDescriptor.dismissOnClick
              ? toggleContainer
              : toggleTitle;

            const closeToggle = () => {
              if (tocShowing) {
                toggleContainer.classList.remove("expanded");
                toggleContents.style.height = "0px";
                tocShowing = false;
              }
            };

            // Get rid of any expanded toggle if the user scrolls
            window.document.addEventListener(
              "scroll",
              throttle(() => {
                closeToggle();
              }, 50)
            );

            // Handle positioning of the toggle
            window.addEventListener(
              "resize",
              throttle(() => {
                elRect = undefined;
                positionToggle();
              }, 50)
            );

            window.addEventListener("quarto-hrChanged", () => {
              elRect = undefined;
            });

            // Process the click
            clickEl.onclick = () => {
              if (!tocShowing) {
                toggleContainer.classList.add("expanded");
                toggleContents.style.height = null;
                tocShowing = true;
              } else {
                closeToggle();
              }
            };
          });
        };

        // Converts a sidebar from a menu back to a sidebar
        const convertToSidebar = () => {
          for (const child of el.children) {
            child.style.opacity = 1;
            child.style.overflow = null;
            child.style.pointerEvents = null;
          }

          const placeholderEl = window.document.getElementById(
            placeholderDescriptor.id
          );
          if (placeholderEl) {
            placeholderEl.remove();
          }

          el.classList.remove("rollup");
        };

        if (isReaderMode()) {
          convertToMenu();
          isVisible = false;
        } else {
          // Find the top and bottom o the element that is being managed
          const elTop = el.offsetTop;
          const elBottom =
            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;

          if (!isVisible) {
            // If the element is current not visible reveal if there are
            // no conflicts with overlay regions
            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToSidebar();
              isVisible = true;
            }
          } else {
            // If the element is visible, hide it if it conflicts with overlay regions
            // and insert a placeholder toggle (or if we're in reader mode)
            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToMenu();
              isVisible = false;
            }
          }
        }
      }
    };
  };

  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
  for (const tabEl of tabEls) {
    const id = tabEl.getAttribute("data-bs-target");
    if (id) {
      const columnEl = document.querySelector(
        `${id} .column-margin, .tabset-margin-content`
      );
      if (columnEl)
        tabEl.addEventListener("shown.bs.tab", function (event) {
          const el = event.srcElement;
          if (el) {
            const visibleCls = `${el.id}-margin-content`;
            // walk up until we find a parent tabset
            let panelTabsetEl = el.parentElement;
            while (panelTabsetEl) {
              if (panelTabsetEl.classList.contains("panel-tabset")) {
                break;
              }
              panelTabsetEl = panelTabsetEl.parentElement;
            }

            if (panelTabsetEl) {
              const prevSib = panelTabsetEl.previousElementSibling;
              if (
                prevSib &&
                prevSib.classList.contains("tabset-margin-container")
              ) {
                const childNodes = prevSib.querySelectorAll(
                  ".tabset-margin-content"
                );
                for (const childEl of childNodes) {
                  if (childEl.classList.contains(visibleCls)) {
                    childEl.classList.remove("collapse");
                  } else {
                    childEl.classList.add("collapse");
                  }
                }
              }
            }
          }

          layoutMarginEls();
        });
    }
  }

  // Manage the visibility of the toc and the sidebar
  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
    id: "quarto-toc-toggle",
    titleSelector: "#toc-title",
    dismissOnClick: true,
  });
  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
    id: "quarto-sidebarnav-toggle",
    titleSelector: ".title",
    dismissOnClick: false,
  });
  let tocLeftScrollVisibility;
  if (leftTocEl) {
    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
      id: "quarto-lefttoc-toggle",
      titleSelector: "#toc-title",
      dismissOnClick: true,
    });
  }

  // Find the first element that uses formatting in special columns
  const conflictingEls = window.document.body.querySelectorAll(
    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
  );

  // Filter all the possibly conflicting elements into ones
  // the do conflict on the left or ride side
  const arrConflictingEls = Array.from(conflictingEls);
  const leftSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return false;
    }
    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        className.startsWith("column-") &&
        !className.endsWith("right") &&
        !className.endsWith("container") &&
        className !== "column-margin"
      );
    });
  });
  const rightSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return true;
    }

    const hasMarginCaption = Array.from(el.classList).find((className) => {
      return className == "margin-caption";
    });
    if (hasMarginCaption) {
      return true;
    }

    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        !className.endsWith("container") &&
        className.startsWith("column-") &&
        !className.endsWith("left")
      );
    });
  });

  const kOverlapPaddingSize = 10;
  function toRegions(els) {
    return els.map((el) => {
      const boundRect = el.getBoundingClientRect();
      const top =
        boundRect.top +
        document.documentElement.scrollTop -
        kOverlapPaddingSize;
      return {
        top,
        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
      };
    });
  }

  let hasObserved = false;
  const visibleItemObserver = (els) => {
    let visibleElements = [...els];
    const intersectionObserver = new IntersectionObserver(
      (entries, _observer) => {
        entries.forEach((entry) => {
          if (entry.isIntersecting) {
            if (visibleElements.indexOf(entry.target) === -1) {
              visibleElements.push(entry.target);
            }
          } else {
            visibleElements = visibleElements.filter((visibleEntry) => {
              return visibleEntry !== entry;
            });
          }
        });

        if (!hasObserved) {
          hideOverlappedSidebars();
        }
        hasObserved = true;
      },
      {}
    );
    els.forEach((el) => {
      intersectionObserver.observe(el);
    });

    return {
      getVisibleEntries: () => {
        return visibleElements;
      },
    };
  };

  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
  const leftElementObserver = visibleItemObserver(leftSideConflictEls);

  const hideOverlappedSidebars = () => {
    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
    if (tocLeftScrollVisibility) {
      tocLeftScrollVisibility(
        toRegions(leftElementObserver.getVisibleEntries())
      );
    }
  };

  window.quartoToggleReader = () => {
    // Applies a slow class (or removes it)
    // to update the transition speed
    const slowTransition = (slow) => {
      const manageTransition = (id, slow) => {
        const el = document.getElementById(id);
        if (el) {
          if (slow) {
            el.classList.add("slow");
          } else {
            el.classList.remove("slow");
          }
        }
      };

      manageTransition("TOC", slow);
      manageTransition("quarto-sidebar", slow);
    };
    const readerMode = !isReaderMode();
    setReaderModeValue(readerMode);

    // If we're entering reader mode, slow the transition
    if (readerMode) {
      slowTransition(readerMode);
    }
    highlightReaderToggle(readerMode);
    hideOverlappedSidebars();

    // If we're exiting reader mode, restore the non-slow transition
    if (!readerMode) {
      slowTransition(!readerMode);
    }
  };

  const highlightReaderToggle = (readerMode) => {
    const els = document.querySelectorAll(".quarto-reader-toggle");
    if (els) {
      els.forEach((el) => {
        if (readerMode) {
          el.classList.add("reader");
        } else {
          el.classList.remove("reader");
        }
      });
    }
  };

  const setReaderModeValue = (val) => {
    if (window.location.protocol !== "file:") {
      window.localStorage.setItem("quarto-reader-mode", val);
    } else {
      localReaderMode = val;
    }
  };

  const isReaderMode = () => {
    if (window.location.protocol !== "file:") {
      return window.localStorage.getItem("quarto-reader-mode") === "true";
    } else {
      return localReaderMode;
    }
  };
  let localReaderMode = null;

  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;

  // Walk the TOC and collapse/expand nodes
  // Nodes are expanded if:
  // - they are top level
  // - they have children that are 'active' links
  // - they are directly below an link that is 'active'
  const walk = (el, depth) => {
    // Tick depth when we enter a UL
    if (el.tagName === "UL") {
      depth = depth + 1;
    }

    // It this is active link
    let isActiveNode = false;
    if (el.tagName === "A" && el.classList.contains("active")) {
      isActiveNode = true;
    }

    // See if there is an active child to this element
    let hasActiveChild = false;
    for (child of el.children) {
      hasActiveChild = walk(child, depth) || hasActiveChild;
    }

    // Process the collapse state if this is an UL
    if (el.tagName === "UL") {
      if (tocOpenDepth === -1 && depth > 1) {
        // toc-expand: false
        el.classList.add("collapse");
      } else if (
        depth <= tocOpenDepth ||
        hasActiveChild ||
        prevSiblingIsActiveLink(el)
      ) {
        el.classList.remove("collapse");
      } else {
        el.classList.add("collapse");
      }

      // untick depth when we leave a UL
      depth = depth - 1;
    }
    return hasActiveChild || isActiveNode;
  };

  // walk the TOC and expand / collapse any items that should be shown
  if (tocEl) {
    updateActiveLink();
    walk(tocEl, 0);
  }

  // Throttle the scroll event and walk peridiocally
  window.document.addEventListener(
    "scroll",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 5)
  );
  window.addEventListener(
    "resize",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 10)
  );
  hideOverlappedSidebars();
  highlightReaderToggle(isReaderMode());
});

// grouped tabsets
window.addEventListener("pageshow", (_event) => {
  function getTabSettings() {
    const data = localStorage.getItem("quarto-persistent-tabsets-data");
    if (!data) {
      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
      return {};
    }
    if (data) {
      return JSON.parse(data);
    }
  }

  function setTabSettings(data) {
    localStorage.setItem(
      "quarto-persistent-tabsets-data",
      JSON.stringify(data)
    );
  }

  function setTabState(groupName, groupValue) {
    const data = getTabSettings();
    data[groupName] = groupValue;
    setTabSettings(data);
  }

  function toggleTab(tab, active) {
    const tabPanelId = tab.getAttribute("aria-controls");
    const tabPanel = document.getElementById(tabPanelId);
    if (active) {
      tab.classList.add("active");
      tabPanel.classList.add("active");
    } else {
      tab.classList.remove("active");
      tabPanel.classList.remove("active");
    }
  }

  function toggleAll(selectedGroup, selectorsToSync) {
    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
      const active = selectedGroup === thisGroup;
      for (const tab of tabs) {
        toggleTab(tab, active);
      }
    }
  }

  function findSelectorsToSyncByLanguage() {
    const result = {};
    const tabs = Array.from(
      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
    );
    for (const item of tabs) {
      const div = item.parentElement.parentElement.parentElement;
      const group = div.getAttribute("data-group");
      if (!result[group]) {
        result[group] = {};
      }
      const selectorsToSync = result[group];
      const value = item.innerHTML;
      if (!selectorsToSync[value]) {
        selectorsToSync[value] = [];
      }
      selectorsToSync[value].push(item);
    }
    return result;
  }

  function setupSelectorSync() {
    const selectorsToSync = findSelectorsToSyncByLanguage();
    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
      Object.entries(tabSetsByValue).forEach(([value, items]) => {
        items.forEach((item) => {
          item.addEventListener("click", (_event) => {
            setTabState(group, value);
            toggleAll(value, selectorsToSync[group]);
          });
        });
      });
    });
    return selectorsToSync;
  }

  const selectorsToSync = setupSelectorSync();
  for (const [group, selectedName] of Object.entries(getTabSettings())) {
    const selectors = selectorsToSync[group];
    // it's possible that stale state gives us empty selections, so we explicitly check here.
    if (selectors) {
      toggleAll(selectedName, selectors);
    }
  }
});

function throttle(func, wait) {
  let waiting = false;
  return function () {
    if (!waiting) {
      func.apply(this, arguments);
      waiting = true;
      setTimeout(function () {
        waiting = false;
      }, wait);
    }
  };
}

function nexttick(func) {
  return setTimeout(func, 0);
}
</script>
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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof 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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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) format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
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.bi-suitcase-fill::before { content: "\f8fd"; }
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.bi-suitcase2-fill::before { content: "\f901"; }
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.bi-vignette::before { content: "\f903"; }
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      <h1 class="title">Tunnels of attention: reconsidering issue competition</h1>
            <p class="subtitle lead">Replication material</p>
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      <div class="quarto-title-meta-heading">Author</div>
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               <p>Emiliano Grossman &amp; Isabelle Guinaudeau </p>
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      <div class="quarto-title-meta-heading">Published</div>
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        <p class="date">4 Oct 2024</p>
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    <h2 id="toc-title">Table of contents</h2>
   
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  <li><a href="#packages-and-data" id="toc-packages-and-data" class="nav-link active" data-scroll-target="#packages-and-data">Packages and data</a></li>
  <li><a href="#appendix-a1-party-data-presentation" id="toc-appendix-a1-party-data-presentation" class="nav-link" data-scroll-target="#appendix-a1-party-data-presentation">Appendix A1: Party data presentation</a></li>
  <li><a href="#figure-1-descriptive" id="toc-figure-1-descriptive" class="nav-link" data-scroll-target="#figure-1-descriptive">Figure 1: descriptive</a></li>
  <li><a href="#appendix-a3-eurobarometer-mip-data-per-country" id="toc-appendix-a3-eurobarometer-mip-data-per-country" class="nav-link" data-scroll-target="#appendix-a3-eurobarometer-mip-data-per-country">Appendix A3: Eurobarometer MIP data per country</a></li>
  <li><a href="#appendix-a4-models-underlying-figure-2" id="toc-appendix-a4-models-underlying-figure-2" class="nav-link" data-scroll-target="#appendix-a4-models-underlying-figure-2">Appendix A4: models underlying figure 2</a></li>
  <li><a href="#figure-2-country-stability-and-overlap" id="toc-figure-2-country-stability-and-overlap" class="nav-link" data-scroll-target="#figure-2-country-stability-and-overlap">Figure 2 : country stability and overlap</a></li>
  <li><a href="#appendix-a4b-models-with-trend-variable-instead-of-election-fixed-effects" id="toc-appendix-a4b-models-with-trend-variable-instead-of-election-fixed-effects" class="nav-link" data-scroll-target="#appendix-a4b-models-with-trend-variable-instead-of-election-fixed-effects">Appendix A4b: models with trend variable instead of election fixed effects</a></li>
  <li><a href="#appendix-a5-government-status" id="toc-appendix-a5-government-status" class="nav-link" data-scroll-target="#appendix-a5-government-status">Appendix A5 : government status</a></li>
  <li><a href="#appendix-a6b-testing-for-the-effect-of-perceived-issue-ownership" id="toc-appendix-a6b-testing-for-the-effect-of-perceived-issue-ownership" class="nav-link" data-scroll-target="#appendix-a6b-testing-for-the-effect-of-perceived-issue-ownership">Appendix A6b: testing for the effect of perceived issue ownership</a></li>
  <li><a href="#appendix-a7-differenced-dependent-variable" id="toc-appendix-a7-differenced-dependent-variable" class="nav-link" data-scroll-target="#appendix-a7-differenced-dependent-variable">Appendix A7: differenced dependent variable</a></li>
  <li><a href="#appendix-a8-topic-and-country" id="toc-appendix-a8-topic-and-country" class="nav-link" data-scroll-target="#appendix-a8-topic-and-country">Appendix A8: topic and country</a></li>
  <li><a href="#appendix-a9-models-underlying-figure-a8" id="toc-appendix-a9-models-underlying-figure-a8" class="nav-link" data-scroll-target="#appendix-a9-models-underlying-figure-a8">Appendix A9: models underlying figure A8</a></li>
  <li><a href="#figure-3-models-with-mips" id="toc-figure-3-models-with-mips" class="nav-link" data-scroll-target="#figure-3-models-with-mips">Figure 3: models with MIPs</a></li>
  <li><a href="#figure-4-comparing-different-drivers-of-overlap" id="toc-figure-4-comparing-different-drivers-of-overlap" class="nav-link" data-scroll-target="#figure-4-comparing-different-drivers-of-overlap">Figure 4: comparing different drivers of overlap</a></li>
  <li><a href="#appendix-a12-distribution-of-manifesto-publication-dates" id="toc-appendix-a12-distribution-of-manifesto-publication-dates" class="nav-link" data-scroll-target="#appendix-a12-distribution-of-manifesto-publication-dates">Appendix A12: Distribution of manifesto publication dates</a></li>
  <li><a href="#appendix-a13-models-underlying-figure-4" id="toc-appendix-a13-models-underlying-figure-4" class="nav-link" data-scroll-target="#appendix-a13-models-underlying-figure-4">Appendix A13: Models underlying figure 4</a></li>
  <li><a href="#figure-5-articles-mentioning-wealth-tax-and-single-german-parties-in-die-welt" id="toc-figure-5-articles-mentioning-wealth-tax-and-single-german-parties-in-die-welt" class="nav-link" data-scroll-target="#figure-5-articles-mentioning-wealth-tax-and-single-german-parties-in-die-welt">Figure 5: Articles mentioning “wealth tax” and single German parties in Die Welt</a></li>
  <li><a href="#appendix-a14-media-attention-to-the-wealth-tax-in-german-media-outlets" id="toc-appendix-a14-media-attention-to-the-wealth-tax-in-german-media-outlets" class="nav-link" data-scroll-target="#appendix-a14-media-attention-to-the-wealth-tax-in-german-media-outlets">Appendix A14: media attention to the “wealth tax” in German media outlets</a></li>
  <li><a href="#appendix-a15-sensitivity-checks-leaving-out-1-topic-at-the-time" id="toc-appendix-a15-sensitivity-checks-leaving-out-1-topic-at-the-time" class="nav-link" data-scroll-target="#appendix-a15-sensitivity-checks-leaving-out-1-topic-at-the-time">Appendix A15: sensitivity checks: leaving out 1 topic at the time</a></li>
  <li><a href="#appendix-a16-media-attention-france-and-the-uk" id="toc-appendix-a16-media-attention-france-and-the-uk" class="nav-link" data-scroll-target="#appendix-a16-media-attention-france-and-the-uk">Appendix A16: Media attention France and the UK</a></li>
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<p>This is the replication material for the article “Tunnels of attention”, published in <em>Comparative Political Studies.</em> Most ot the analyses are contained in this file, but we “source” one additional script, dedicated to the analysis of issue ownership in Appendix 6b.</p>
<section id="packages-and-data" class="level2">
<h2 class="anchored" data-anchor-id="packages-and-data">Packages and data</h2>
<p>This chunk loads all the relevant R packages and datasets. There is several different datasets:</p>
<ol type="1">
<li><p>data_eb : a dataset pairing party attention data with relevant Eurobarometer “Most important problem” data</p></li>
<li><p>data_complete: the dataset with complete party attention data across eight countries.</p></li>
<li><p>data_complete_with_early: same as 2. but limited to the four countries for which we collected the manifesto publication dates.</p></li>
<li><p>ms: manifesto_publication dates.</p></li>
<li><p>media_data: this dataset contains media data for France and Germany, used in the analysis in Appendix A13.</p></li>
<li><p>mip_stats: summary data on the availability of MIP data in the Eurobarometer for Appendix A3</p></li>
<li><p>media_wealth_germany: media data on the German Wealth tax used for figure 5 and appendix 14</p></li>
</ol>
<p>Three other - relatively - minor datasets are loaded in Appendix A1 and Appendix A6b.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">### packages and data</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(fixest)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggpubr)</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggeffects)</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modelsummary)</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sandwich)</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lmtest)</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(psych)</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lavaan)</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lavaanPlot)</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readxl)</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(semPlot)</span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(kableExtra)</span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(DiagrammeRsvg)</span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(zoo)</span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readxl)</span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(margins)</span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggrepel)</span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a><span class="do">### data</span></span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>deb <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="st">&quot;data_eb.csv&quot;</span>) <span class="co"># data for Eurobarometer MIP analyses</span></span>
<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a>dc <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="st">&quot;data_complete.csv&quot;</span>) <span class="co"># complete data</span></span>
<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a>dc_early <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="st">&quot;data_complete_with_early.csv&quot;</span>) <span class="co"># complete data with data on first manifesto published for 4 countries</span></span>
<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a>ms<span class="ot">=</span> <span class="fu">read_excel</span>(<span class="st">&quot;manifesto_dates_data.xlsx&quot;</span>) <span class="co"># manifesto publication dates </span></span>
<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;media_data.Rdata&quot;</span>)</span>
<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a><span class="fu">load</span>(<span class="st">&quot;mip_stats.Rdata&quot;</span>)</span>
<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a>mw <span class="ot">=</span> <span class="fu">read.csv</span>(<span class="st">&quot;media_wealth_germany.csv&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="appendix-a1-party-data-presentation" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a1-party-data-presentation">Appendix A1: Party data presentation</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>ps <span class="ot">=</span> <span class="fu">read_csv</span>(<span class="st">&quot;stats.csv&quot;</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>ps <span class="ot">=</span> ps[,<span class="sc">-</span><span class="dv">1</span>]</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>ps<span class="sc">$</span>Country <span class="ot">=</span> <span class="fu">toupper</span>(ps<span class="sc">$</span>Country)</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>gridExtra<span class="sc">::</span><span class="fu">grid.table</span>(ps,<span class="at">rows =</span> <span class="cn">NULL</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="figure-1-descriptive" class="level2">
<h2 class="anchored" data-anchor-id="figure-1-descriptive">Figure 1: descriptive</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>deb <span class="sc">%&gt;%</span> <span class="fu">filter</span>(topic<span class="sc">==</span><span class="dv">107</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(cn<span class="sc">!=</span><span class="st">&quot;SE&quot;</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(cn,<span class="at">year =</span> year) <span class="sc">%&gt;%</span> </span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">party =</span> <span class="fu">mean</span>(party_share,<span class="at">na.rm=</span>T),</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>                                  <span class="at">MIP =</span> <span class="fu">mean</span>(mip_share,<span class="at">na.rm=</span>T)) <span class="sc">%&gt;%</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(cn,year, party ,MIP) <span class="sc">%&gt;%</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">na.omit</span>() <span class="sc">|&gt;</span></span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">names_to =</span> <span class="st">&quot;name&quot;</span>,<span class="at">values_to =</span> <span class="st">&quot;value&quot;</span>,<span class="sc">-</span><span class="fu">c</span>(<span class="st">&#39;cn&#39;</span>,<span class="st">&#39;year&#39;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(year,value,<span class="at">linetype =</span> name)) <span class="sc">+</span> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_step</span>() <span class="sc">+</span> <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>(),<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(.<span class="sc">~</span>cn) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;share of attention&quot;</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a3-eurobarometer-mip-data-per-country" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a3-eurobarometer-mip-data-per-country">Appendix A3: Eurobarometer MIP data per country</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>ttf <span class="ot">=</span> gridExtra<span class="sc">::</span><span class="fu">ttheme_default</span>(<span class="at">core=</span><span class="fu">list</span>(<span class="at">fg_params=</span><span class="fu">list</span>(<span class="at">hjust=</span><span class="dv">0</span>, <span class="at">x=</span><span class="fl">0.01</span>)))</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> test <span class="sc">%&gt;%</span> <span class="fu">select</span>(Country,<span class="at">Year =</span> year) <span class="sc">%&gt;%</span> </span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> gridExtra<span class="sc">::</span><span class="fu">grid.table</span>(<span class="at">theme=</span>ttf,<span class="at">rows =</span> <span class="cn">NULL</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a4-models-underlying-figure-2" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a4-models-underlying-figure-2">Appendix A4: models underlying figure 2</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>dc<span class="sc">$</span>year_country <span class="ot">=</span> <span class="fu">paste</span>(dc<span class="sc">$</span>year,dc<span class="sc">$</span>country_name, <span class="at">sep =</span> <span class="st">&quot;_&quot;</span>)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> cns <span class="ot">=</span> <span class="fu">unique</span>(dc<span class="sc">$</span>cn)</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> cns <span class="ot">=</span> cns[cns<span class="sc">!=</span><span class="st">&quot;sw&quot;</span>]</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> res2 <span class="ot">=</span>  </span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>   models <span class="ot">=</span> <span class="fu">lapply</span>(cns, <span class="cf">function</span>(x){</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>     form <span class="ot">=</span> party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others<span class="sc">|</span>year_country  </span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>     form <span class="ot">=</span> <span class="fu">feols</span>(form,<span class="at">data =</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn<span class="sc">==</span>x))</span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>     form</span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>   })</span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a> res2 <span class="ot">=</span> res2[<span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">7</span>)]</span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">names</span>(res2) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;Italy&quot;</span>,<span class="st">&quot;Netherlands&quot;</span>,</span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;United Kingdom&quot;</span>)</span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a> modelsummary<span class="sc">::</span><span class="fu">modelsummary</span>(res2 , <span class="at">estimate =</span> <span class="st">&quot;{estimate}{stars}&quot;</span>,</span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>                            <span class="at">statistic =</span> <span class="cn">NULL</span>,</span>
<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a>                            <span class="co">#coef_omit = c(-1,-2,-3,),</span></span>
<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a>                            <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;(Intercept)&quot;</span> <span class="ot">=</span> <span class="st">&quot;Constant&quot;</span>,</span>
<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a>                                            <span class="st">&quot;party_share_last&quot;</span><span class="ot">=</span><span class="st">&quot;Stability&quot;</span>,</span>
<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a>                                            <span class="st">&quot;attention_others&quot;</span><span class="ot">=</span><span class="st">&quot;Overlap&quot;</span>),</span>
<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a> <span class="co">#                                           &quot;year&quot; = &quot;Year (trend)&quot;),</span></span>
<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a>                            <span class="co">#vcov = &quot;robust&quot;, </span></span>
<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a>                            <span class="at">cluster =</span> <span class="sc">~</span>party,</span>
<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a>                            <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|AIC|BIC&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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              <tr>
                <th scope="col"> </th>
                <th scope="col">Belgium</th>
                <th scope="col">Denmark</th>
                <th scope="col">France</th>
                <th scope="col">Germany</th>
                <th scope="col">Italy</th>
                <th scope="col">Netherlands</th>
                <th scope="col">United Kingdom</th>
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                  <td>Stability       </td>
                  <td>0.297***        </td>
                  <td>0.353***        </td>
                  <td>0.260**         </td>
                  <td>0.400**         </td>
                  <td>0.313***        </td>
                  <td>0.310*          </td>
                  <td>0.157*          </td>
                </tr>
                <tr>
                  <td>Overlap         </td>
                  <td>0.694***        </td>
                  <td>0.600***        </td>
                  <td>0.620***        </td>
                  <td>0.466***        </td>
                  <td>0.576***        </td>
                  <td>0.637***        </td>
                  <td>0.782***        </td>
                </tr>
                <tr>
                  <td>Num.Obs.        </td>
                  <td>2068            </td>
                  <td>865             </td>
                  <td>790             </td>
                  <td>462             </td>
                  <td>2816            </td>
                  <td>1672            </td>
                  <td>880             </td>
                </tr>
                <tr>
                  <td>R2              </td>
                  <td>0.537           </td>
                  <td>0.608           </td>
                  <td>0.388           </td>
                  <td>0.571           </td>
                  <td>0.366           </td>
                  <td>0.409           </td>
                  <td>0.471           </td>
                </tr>
                <tr>
                  <td>R2 Adj.         </td>
                  <td>0.535           </td>
                  <td>0.604           </td>
                  <td>0.381           </td>
                  <td>0.563           </td>
                  <td>0.363           </td>
                  <td>0.406           </td>
                  <td>0.466           </td>
                </tr>
                <tr>
                  <td>R2 Within       </td>
                  <td>0.537           </td>
                  <td>0.607           </td>
                  <td>0.386           </td>
                  <td>0.569           </td>
                  <td>0.366           </td>
                  <td>0.409           </td>
                  <td>0.471           </td>
                </tr>
                <tr>
                  <td>R2 Within Adj.  </td>
                  <td>0.537           </td>
                  <td>0.606           </td>
                  <td>0.384           </td>
                  <td>0.567           </td>
                  <td>0.366           </td>
                  <td>0.408           </td>
                  <td>0.470           </td>
                </tr>
                <tr>
                  <td>RMSE            </td>
                  <td>0.03            </td>
                  <td>0.02            </td>
                  <td>0.04            </td>
                  <td>0.02            </td>
                  <td>0.07            </td>
                  <td>0.03            </td>
                  <td>0.05            </td>
                </tr>
                <tr>
                  <td>Std.Errors      </td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                  <td>by: year_country</td>
                </tr>
                <tr>
                  <td>FE: year_country</td>
                  <td>X               </td>
                  <td>X               </td>
                  <td>X               </td>
                  <td>X               </td>
                  <td>X               </td>
                  <td>X               </td>
                  <td>X               </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</section>
<section id="figure-2-country-stability-and-overlap" class="level2">
<h2 class="anchored" data-anchor-id="figure-2-country-stability-and-overlap">Figure 2 : country stability and overlap</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>res2out <span class="ot">=</span> <span class="fu">lapply</span>(res2,<span class="cf">function</span>(x) <span class="fu">summary</span>(x)<span class="sc">$</span>coeftable[<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>,<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>])</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> test <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">do.call</span>(rbind,res2out))</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> test<span class="sc">$</span>var2 <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Stability&quot;</span>,<span class="st">&quot;Overlap&quot;</span>)</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> test<span class="sc">$</span>cn <span class="ot">=</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;United Kingdom&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;Netherlands&quot;</span>,<span class="st">&quot;Italy&quot;</span>),<span class="at">each=</span><span class="dv">2</span>)</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> test <span class="sc">%&gt;%</span> <span class="co">#filter(cn != &quot;sw&quot;) %&gt;%</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>   <span class="fu">ggplot</span>(<span class="fu">aes</span>(cn,Estimate,<span class="at">color=</span>var2)) <span class="sc">+</span> </span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>   <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Estimate<span class="sc">-</span>Std..Error,<span class="at">ymax=</span>Estimate<span class="sc">+</span>Std..Error),</span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>                   <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width=</span>.<span class="dv">5</span>)) <span class="sc">+</span></span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>   <span class="fu">coord_flip</span>() <span class="sc">+</span> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span><span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>   <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;coefficients&quot;</span>) <span class="sc">+</span> <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>   <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;black&quot;</span>,<span class="st">&quot;gray&quot;</span>)) <span class="sc">+</span></span>
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a>   <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>(),<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a4b-models-with-trend-variable-instead-of-election-fixed-effects" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a4b-models-with-trend-variable-instead-of-election-fixed-effects">Appendix A4b: models with trend variable instead of election fixed effects</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>res2 <span class="ot">=</span>  </span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>  models <span class="ot">=</span> <span class="fu">lapply</span>(cns, <span class="cf">function</span>(x){</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>   form <span class="ot">=</span> party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others <span class="sc">+</span> (year) </span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>   form <span class="ot">=</span> <span class="fu">lm</span>(form,<span class="at">data =</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn<span class="sc">==</span>x))</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>   form</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> })</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> res2 <span class="ot">=</span> res2[<span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">7</span>)]</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(res2) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;Italy&quot;</span>,<span class="st">&quot;Netherlands&quot;</span>,</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a>                <span class="st">&quot;United Kingdom&quot;</span>)</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>modelsummary<span class="sc">::</span><span class="fu">modelsummary</span>(res2 , <span class="at">estimate =</span> <span class="st">&quot;{estimate}{stars}&quot;</span>,</span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>                            <span class="at">statistic =</span> <span class="cn">NULL</span>,</span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a>                            <span class="co">#coef_omit = c(-1,-2,-3,),</span></span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a>                           <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;(Intercept)&quot;</span> <span class="ot">=</span> <span class="st">&quot;Constant&quot;</span>,</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a>                                           <span class="st">&quot;party_share_last&quot;</span><span class="ot">=</span><span class="st">&quot;Stability&quot;</span>,</span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a>                                           <span class="st">&quot;attention_others&quot;</span><span class="ot">=</span><span class="st">&quot;Overlap&quot;</span>,</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a>                                           <span class="st">&quot;year&quot;</span> <span class="ot">=</span> <span class="st">&quot;Year (trend)&quot;</span>),</span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a>                            <span class="at">vcov =</span> <span class="st">&quot;robust&quot;</span>,<span class="co"># cluster = ~party,</span></span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a>                            <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|AIC|BIC&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Belgium</th>
                <th scope="col">Denmark</th>
                <th scope="col">France</th>
                <th scope="col">Germany</th>
                <th scope="col">Italy</th>
                <th scope="col">Netherlands</th>
                <th scope="col">United Kingdom</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant    </td>
                  <td>0.118   </td>
                  <td>0.070   </td>
                  <td>-0.393  </td>
                  <td>-0.285  </td>
                  <td>-0.196  </td>
                  <td>0.066   </td>
                  <td>-0.522  </td>
                </tr>
                <tr>
                  <td>Stability   </td>
                  <td>0.281***</td>
                  <td>0.350***</td>
                  <td>0.248** </td>
                  <td>0.386***</td>
                  <td>0.315***</td>
                  <td>0.301***</td>
                  <td>0.151+  </td>
                </tr>
                <tr>
                  <td>Overlap     </td>
                  <td>0.707***</td>
                  <td>0.602***</td>
                  <td>0.627***</td>
                  <td>0.480***</td>
                  <td>0.569***</td>
                  <td>0.640***</td>
                  <td>0.783***</td>
                </tr>
                <tr>
                  <td>Year (trend)</td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                  <td>0.000   </td>
                </tr>
                <tr>
                  <td>Num.Obs.    </td>
                  <td>2068    </td>
                  <td>865     </td>
                  <td>790     </td>
                  <td>462     </td>
                  <td>2816    </td>
                  <td>1672    </td>
                  <td>880     </td>
                </tr>
                <tr>
                  <td>R2          </td>
                  <td>0.535   </td>
                  <td>0.607   </td>
                  <td>0.387   </td>
                  <td>0.567   </td>
                  <td>0.365   </td>
                  <td>0.406   </td>
                  <td>0.469   </td>
                </tr>
                <tr>
                  <td>R2 Adj.     </td>
                  <td>0.534   </td>
                  <td>0.606   </td>
                  <td>0.384   </td>
                  <td>0.564   </td>
                  <td>0.365   </td>
                  <td>0.404   </td>
                  <td>0.467   </td>
                </tr>
                <tr>
                  <td>Log.Lik.    </td>
                  <td>4126.196</td>
                  <td>2083.627</td>
                  <td>1510.639</td>
                  <td>1117.884</td>
                  <td>3645.079</td>
                  <td>3383.020</td>
                  <td>1361.268</td>
                </tr>
                <tr>
                  <td>RMSE        </td>
                  <td>0.03    </td>
                  <td>0.02    </td>
                  <td>0.04    </td>
                  <td>0.02    </td>
                  <td>0.07    </td>
                  <td>0.03    </td>
                  <td>0.05    </td>
                </tr>
                <tr>
                  <td>Std.Errors  </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                  <td>HC3     </td>
                </tr>
        </tbody>
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</div>
</section>
<section id="appendix-a5-government-status" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a5-government-status">Appendix A5 : government status</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>cabtest <span class="ot">=</span> <span class="cf">function</span>(x){</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>   dn <span class="ot">=</span> deb <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>   dn<span class="sc">$</span>cabinet_party <span class="ot">=</span> <span class="fu">factor</span>(dn<span class="sc">$</span>cabinet_party)</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>   <span class="co">#test1 = lm(party_share ~ party_share_last * cabinet_party + scale(p2_share)  + scale(share) + party , data = dn )</span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>   test2 <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others  <span class="sc">*</span> cabinet_party <span class="sc">+</span> mip_share  , <span class="at">data =</span> dn )</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>   <span class="co">#out1 = ggeffect(test1,terms=c(&quot;party_share_last&quot;,&quot;cabinet_party&quot;))</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>   <span class="co">#out1$var = &quot;party_share_last&quot;</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>   out2 <span class="ot">=</span> <span class="fu">ggpredict</span>(test2,<span class="at">terms=</span><span class="fu">c</span>(<span class="st">&quot;attention_others[0,.1,]&quot;</span>,<span class="st">&quot;cabinet_party&quot;</span>))</span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>   <span class="co">#out2$var = &quot;p2_share&quot;</span></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>   out <span class="ot">=</span> out2 <span class="co">#bind_rows(out1,out2)</span></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>   out<span class="sc">$</span>cn<span class="ot">=</span>x</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>   out</span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> <span class="co">#data$cabinet_party[is.na(data$cabinet_party)] = 0</span></span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> cns<span class="ot">=</span> <span class="fu">unique</span>(deb<span class="sc">$</span>cn)</span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> cns<span class="ot">=</span>cns[cns<span class="sc">!=</span><span class="st">&#39;SE&#39;</span>]</span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> out <span class="ot">=</span> <span class="fu">lapply</span>(cns,cabtest)</span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> out2 <span class="ot">=</span> <span class="fu">do.call</span>(rbind,out)</span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a> out2 <span class="sc">%&gt;%</span> <span class="fu">filter</span>(x<span class="sc">==</span>.<span class="dv">1</span>) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn<span class="sc">!=</span><span class="st">&quot;SE&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>   <span class="fu">ggplot</span>(<span class="fu">aes</span>(cn,predicted,<span class="at">color =</span> group)) <span class="sc">+</span> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>,<span class="at">linetype =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a>   <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>conf.low,<span class="at">ymax=</span>conf.high),<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width=</span>.<span class="dv">3</span>)) <span class="sc">+</span></span>
<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a>   <span class="fu">theme_minimal</span>() <span class="sc">+</span> <span class="fu">scale_color_grey</span>() <span class="sc">+</span> <span class="fu">coord_flip</span>() <span class="sc">+</span> </span>
<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>   <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Attention others&quot;</span>,<span class="at">y =</span> <span class="st">&quot;Attention at present election&quot;</span>, <span class="at">color =</span> <span class="st">&quot;Incumbent cabinet member&quot;</span>) <span class="sc">+</span> </span>
<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a>   <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a6b-testing-for-the-effect-of-perceived-issue-ownership" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a6b-testing-for-the-effect-of-perceived-issue-ownership">Appendix A6b: testing for the effect of perceived issue ownership</h2>
<p>As this document is already very long, we created additional scripts for some of the robustness checked that were added during the review process.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">&quot;issue_ownership.R&quot;</span>)</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>out2 <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">io =</span> <span class="fu">factor</span>(io)) <span class="sc">|&gt;</span></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">filter</span>(factor <span class="sc">!=</span> <span class="st">&quot;io&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(io,AME,<span class="at">color =</span> vars)) <span class="sc">+</span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> AME<span class="sc">-</span>SE,<span class="at">ymax=</span>AME<span class="sc">+</span>SE),<span class="at">position =</span> <span class="fu">position_dodge</span>(.<span class="dv">3</span>)) <span class="sc">+</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Issue ownership&quot;</span>,<span class="at">color =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">facet_wrap</span>(. <span class="sc">~</span> cns) <span class="sc">+</span></span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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Ki26aqHpfZ2RkD1EFBtBFTbZNXDYfsFJaB6CKi2kK+xEdD1ylUPhzsoKAHVQ0C1EVBtxaqHw10UlIDqIaDaCKg2AuqLzfnXriK2uy8C2hwB1ZaveljXzs4IqJ6ZZe/8XRjMZz0Cqo2A+mJz/guuIra3L52Atl0ifwioNgLqi835L7qK2Nq+CGhzBFQbAfXF5vwXvgje1r5kAtr6ixX+hLzThYCux0UkX2zOf/F/QVval0hAd/BihT8h73QhoOsRUF9szr+HAd3FixX+hLzTJVhAjcwmRUB9sRzQ+is4O3oJJ8h8dvEvukch73QJFFBD0+FOJH8I6FoKAd3NqYI/Ic9wwgTU0nS4F94fAroWAW2udwG1NR4+jckbArqWQEB39JP6E/JtMiECamw8fB6oN5YD2p+LSAR0FQLamL2/0qPJ/h5gM7V/bVvay1xAW9rPCnMl2v0SXOxo2fWAtrOXVYzOZ0c4A9XDGai2np2BGp3PjhBQPX27E8nY8dm3i0gEdBUCqqdv98IbOz4JqDgC6ovxgPbl05iMHZ99C6jV+ewGAdXTu88DtXV89u5OJAK6AgHV07+/0sPS4dm/e+EJ6AoEVE//AmrzXuvefBqTrX4SUG8I6FoiAbX4aT+ZXb/Gwr3w6xFQXwjoWjIB5RPpF5MIqNVnCDtAQPX0M6B29DCgZp8htI6A6iGg2noZUEMIqC82509AxRFQbQTUF5vzJ6DiCKg2AuqLzfkTUHEhD1ACuh4B9cXm/AmoOAKqjYD6YnP+BFQcAdVGQH2xOX8CKo6AaiOgvticPwEVR0C1EVBfbM6fgIojoNoIqC82509AxRFQbQTUF5vzJ6DiCKg2ewE9vzWKouuHs5t+9udRdKm6Kd+fy+O7sjl/AiqOgGozF9CzvSj1wrvTTe9kW6JLr83tz+HxndmcPwEVR0C1mQvoQXTlMD7dj648LLccR5e+HKebZqOa78/h8Z3ZnD8BFUdAtVkL6Mkoy+TZ3uR883w/uhlnm/JfZ/fX/PHddeADe9tGQJsjoNqsBfQoulr8eqPYcrZXnHkeTDZN9tf88d2Z/ysj2kdAmyOg2qwF9KA4zTwuQlr5RxoB5a8kWIaANkdAtRkL6Pl+8dT9ZDR9ETQ386x+sr/Gj7+FBX+li4WCElBtBFRbdwJ6ND0nvV16sHu1v5Y1wAp0zQU09IJQVQ9o6PWgymtAa9fcj0XexsTfa70cZ6DNcQaqrStnoMejSzfn99f48beQ/z85rNvlElwQUG0EVFtHAnq04PyTgG6CgGojoNqMBXTJVfh3FvaTgG6AgGojoNqsBbR8/+fRzHuWzg+iy/WbkPL9NX98dwR0LQLaHAHVZi2g83ciZXd3Plz4zVxEWo+AaiOg2qwF9Hw/uly7F/5oWT8J6AYIqDYCqs1aQOPTmU9jOhkl56HFxzOl6jcnEdD1CKg2AqrNXEDj01tJKq9n55xZQI8jqYByJ9JyBLQ5AqrNXkCb7K/lx6/gXvi1CGhzBFQbAfWFT2Nai4A2R0C1EVBf+DzQtQhocwRUGwH1xeb8Cag4AqqNgPpic/4EVBwB1UZAfbE5fwIqjoBqI6C+2Jw/ARVHQLURUF9szp+AiiOg2gioLzbnT0DFEVBtBNQXm/MnoOIIqDYC6ovN+RNQcQRUGwH1xeb8Cag4AqqNgPpic/4EVBwB1UZAfbE5fwIqjoBqI6C+2Jw/ARVHQLURUF9szp+AiiOg2gioLzbnT0DFEVBtBNQXm/MnoOIIqLZuBxRimI845qONgPYb8xHHfLTtNqAA0FkEFAAcEVAAcERAAcARAQUARwQUABwRUABwREABwBEBBQBHBBQAHBFQAHBEQAHAEQEFAEcEFAAc8XF2PcN8xDEfbbUDiID2DPMRx3y01Q4gAtozzEcc89FWO4AIaM8wH3HMR1vtACKgPcN8xDEfbbUDiID2DPMRx3y01Q4gAtozzEcc89FWO4AIaM8wH3HMR1vtACKgPcN8xDEfbbUDiID2DPMRx3y01Q4gAtozzEcc89FWO4AIaM8wH3HMR1vtACKgPcN8xDEfbbUDiID2DPMRx3y01Q4gAtozzEcc89FWO4AIaM8wH3HMR1vtACKga/3N+3H8y9CL8KZ787n9djqe1//rr0Kvw4/uzWfi67/4w/dDr2FrtQOIgK7zRvpjEFBhWUDfiH8Xeh1+dG8+EwS0udA/7vbejn/3Vug1eNS5+RQIqD4C2lzoH3d7b3fo9PM2AZXX0fmkCGhzoX/cbWVP4JNj8434l997P/6Xvwu9nq11bD6p5D9xr/8wH1MHdG0+d+Off+83cfx/fkBAXYT+cbc1Deiv34/j39t/Mt+x+aQIqLK78f/LfpKknQS0udA/7vbyp/BvdKKet7saUJ7Cy7obpxcRkqdvPyegDkL/uNsrA/qx/afvqc7Nh4Bqu5ufedxNhkRAmwv9426vDGg3TkAJqLquzeduPpi3k18IaHOhf9ztEVB1BFTZ3fxdLATUTegfd3tlQDt5fHZgPgRUGwHdSugfd3sEVB0BVUZAtxL6x90eAVVHQJUR0K2E/nG3R0DVEVBlBHQroX/c7RFQdUVAO3mVL/RqtkdAtxL6x90eAVWXTegb3bhRjICqqx1ABHQdAqoun9DfvE9AFRHQrYT+cVHDfMQxH221A4iA9gzzEcd8tNUOIALaM8xHHPPRVjuACGjPMB9xzEdb7QAioD3DfMQxH221A4iA9gzzEcd8tNUOIALaM8xHHPPRVjuACGjPMB9xzEdb7QAioD3DfMQxH221A4iA9gzzEcd8tNUOIALaM8xHHPPRVjuACGjPMB9xzEdb7QAioD3DfMQxH221A4iA9g3zEcd8tO04oC0/fsWDXe7Mn10uey6gO9w389lAPaC73LfRAYWcDwEVQEC1EVBtBNQXm/MnoOIIqDYC6ovN+RNQcQRUGwH1xeb8Cag4AqqNgPpic/4EVBwB1UZAfbE5fwIqjoBqI6C+2Jw/ARVHQLURUF9szp+AiiOg2gioLzbnT0DFEVBtBNQXm/MnoOIIqDYC6ovN+RNQcQRUGwH1xeb8Cag4AqqNgPpic/4EVBwB1UZAfbE5fwIqjoBqI6C+2Jw/ARVHQLURUF9szp+AiiOg2gioLzbnT0DFEVBtBNQXm/MnoOIIqDYC6ovN+Xc5oOPx7FdW5lNdNQEVR0B9sTl/AqqGgJpCQH2xOX8CqoaAmtLtgD6AkrmAtru78bjdx29HyFXXAxpuJViEM1A9nIGK4QzUlG6fgbb8+BU2509A1RBQUwioLzbnT0DVEFBTCKgvNuff4YCOx5UWGZlPbdUEVBwB9cXm/Dsb0HGp3GBiPnOr7nJAQ55rb0HnP3AEVEBHAzoe11tkYT7zq+5wQIOea7sL+cSGgOrpZkDH47kWGZjPglV3NqCBz7VdSf0HjoAK6GRAx+P5FunPZ9GquxrQ0OfajrT+A0dABRBQGT0KaPBzbTdi8zEe0E68Bt7JgI7r0o3y81m46m4GNPx/KpyozYeAhkBARRFQdWrzIaAhEFBR/QmowE/qIvy/VgRUAAEVJZAVArpK+H+tCKiAHgQ0/HMtJ4tWTUB1EFC/CGhTBHQlAqqNgPpFQJsioCv1JqACP6mT4P9adSqg08nn9OefCbls7kRabcGqCagQAupP/d9zC/OPQy+be+HXmF81ARVCQL0J/W+6o8DL5tOY1gn832XuRFotdPY7E9DuzL+zAeXzQB1wL/wagQ/7rgS0Q89AOhxQPpG+MT6NaZ2w2fcS0PNboyi6fljZ9E6y6U++PL8/l8ffQPASuQm+bAK6gZ4EtCtXYc0F9GwvSr3w7nTTab4p+tLc/hwefwPjumyr/PzDL5uAbqA3Ae3G+wDNBfQgunIYn+5HVx6WW873o8uH8fnfRpdeq+/P4fE3EL5ETsIvm4BuoD8BrbEyoCprAT0ZZeeeZ3vTWh4Xp6NH0dX6/po//ibCl8hJ+GUT0A0QUFOsBbSs5FF0o9iSnIDeXLa/5o+/ifAlchJ+2QR0AwTUFGsBPShqeTw53Tzbm309tLq/5o+/kUUhMjD/4MsmoBsgoKYYC+j5fvHU/WRUvgia/u6nX4iiy1+b31/jx99M8BK5Cb7sHQe0Sn8+CxFQbZ0I6K38Knz5pD7ZT+FBO2olamkv3gVf9lxAd78ErFIPaOj1oMprQMsn7sfpG5gexufv7OwqfPhbehyFXjZnoM1xBqrN/hnocXnqebCrq/Bxh+5EI6DiCKg2+wE9Gc1tmuyv8eNvbC5ENuYfdtkEtDkCqs1YQBdchZ88mZ8+q5/sr/njb66SodjM/EMum4A2R0C1WQto+f7P2feBFmegx9EOz0DjjtyJRkDFEVBt1gK64E6k8rXPg+ll+HJ/zR+/AQLaFAFtjoBqsxbQ/Mb3yr3wJ6P0w5l2ehU+Q0CbIqDNEVBt1gJafvRSdh5aXD86HmWbLt2c25/LIjdGQJsioM0RUG3mAhqfpm+bv56df5YX4E/Tjwj94uHctxLQBQioKQRUm72ANtlfq49OQJsioM0RUG0E1Beb8yeg4gioNgLqi835E1BxBFQbAfXF5vwJqDgCqo2A+mJz/gRUHAHVRkB9sTl/AiqOgGqTCujjf/iP/+l+ZctHn/6jZ77rvj/nP+nA5vwJqDgCqk0qoB+9OPhEmsvHf/mZP71f2eK4P+c/6cDm/AmoOAKqTTKg02wS0LYRUG0EVBsB9cXm/AmoOAKqjYD6YnP+BFQcAdVGQH2xOX8CKo6AaiOgvticPwEVR0C1EVBfbM6fgIojoNoIqC82509AxRFQbQTUF5vzJ6DiCKg2AuqLzflXlj0ctrsvAtocAdVGQH2xOX8CKo6Aaut2QB+gieGw3cefC2i7u0NT9YCGXg+qNgjoPM5AW8UZqDbOQLWpnYES0B0joNoIqDapgD7+y8/M+9P7sSsO0PUIqDYCqk0qoN731/LjV9icPwEVR0C1EVBfbM6fgIojoNoIqC82509AxRFQbQTUF5vzJ6DiCKg2qYA+/g9cRNo1AqqNgGrTCujLvI1p1wioNgKqTSqg8Z3B4Ik/quFv5WwVAdVGQLVpBfRecsb57Lf97c/bI23A5vxnlz0ctlxQAtocAdWmFdD4x58eeGwoB+h6k2UPS+3ti4A2R0C1iQU08d5LSUIvPPsdH/vz8Bgbszn/ybKHw/YLSkCbI6Da9AIax4/f/KSfhnKArlcsezjcQUEJaHMEVJtiQBO/ffOih4ZygK6XL3s43EVBCWhzBFSbaEATH76SNfSzWzSUA3Q9AqqNgGrTDWjix6/wPtC2Zcse1rWzLwLaHAHVJh3QD79ykYC2jIBqI6DadAOavww6uPAct3K2iYBqI6DaRAOaX4jf9ioSB+h6BFQbAdWmGNDH2VtBPbyPiQN0PS4iaSOg2vQC+t6nV9fz/NYoiq4f1jefjK48nNufwxqd2Zw/ARVHQLWJBTS77r7y3PNsL0q98G518/l+REBdEFBtBFSbVkDvrX/mfhBdOYxP53J5FBFQJ9yJpI2AatMK6J3B4MnVH6h8MsrOPc/2Lr1W3UxA3XAvvDYCqk0qoBt8oPJRdLX49cbM1uQJ/F/wGqgTPo1JGwHVJhXQj15cG9CD6Gb263ER0nLrVS4iueHzQLURUG1SAV3vfL946l7p5XHy9J2AuuET6bURUG1dCGj2gujshtulB2hiOGz38ecC2u7u0FQ9oKHXgyqHgD5+81/NPoWfCej0jUwH6euhnIG64QxUG2eg2rTPQNO/4eMTSwI66eVRdv2dgLohoNoIqDbhgD7OP01kXUBPRtkmAuqGgGojoNpkA5r/9XKDQe1t9fNX4Y+iifrtSRyg6xFQbQRUm2ZAi5PPwTOv1v9J+f7P6ftACehWCKg2AqpNMaDvfXrRO0BzS+5E4im8KwKqjYBqkwvob7+Zf47ypy4uDOj5fnR50b3wBNQRAdVGQLWJBTT/JNDBs99O70pa+Jd5nM58GlNx/ShDQN0QUG0EVJtUQPNbOZ989X7++8V/G9LpraSf17NYEtDtEVBtBFSbWkCf/Kv7k99v8dfJFfvb8s83YnP+BFQcAdWmFtDBM0VBCehu7HLZBLQ5AqpNKqCPv5m9AvrE5+8T0F0hoNoIqDapgMaTq0jPfJuA7gYB1VZZ9o5fYyGg66kFdPom+sXvA222vy3/fCM2509AxRFQbXoBTXz4Sl7Q536y5f62++PN2Jw/ARVHQLVJBjSePJW/8Ln7y79n/f62+LON2Zw/ARVHQLWpBnT6VP4p9yfyHKDrEVBtBFSbbkDj4qn8Fq+EcoCuR0C1EVBt0gGN06fyBLRVBFQbAdWmHtDt9tfy41fYnD8BFUdAtRFQX2zOn4CKI6DaCKgvNudPQMURUG0E1Beb8yeg4noZ0OGw7Z/UGwLqSxcO0JYR0OZ6GNBhaae7d0RAfenCAdoyAtpc/wI6HFoqKAH1pQsHaMsIaHO9C+hwaKqgBNSXLhygLSOgzfUtoMOhrYISUF+6cIC2jIA2R0C1dTugD6BkLqDBVjIcBtv1Ntpedj2g7e5tkWHd7pcgjDNQPf08AzVwalPo2RnoXEB3ugQH3T4DbfnxKwjoWgS0OQKqjYD6QkDXIqDNEVBtBNQXAroWAW2uZwHlIl7atKUAABw+SURBVNIqBFQPAdVGQLURUF8I6FoEtDkCqo2A+kJA15IJqIlDM9e3gHIn0goEVE8PA2ro8Nxx9xUCyr3wyxFQPf0LqNUDdAerlgio3f/AtY6A6uldQK0+RdzFqjUCavcllrYRUD19C6jVixQ7WbVKQA0hoL7YnD8BFbfTZRPQxgioL114m0zLBAI6rNvlElwQUG0E1BcCuhYBbW6nyyagjRFQXwjoWgS0OQKqjYD6QkDXIqDNEVBtBNSXLtzp0jKBgBq9iERAVRFQXzpwp0vbCGhzXETSRkB9MX+nS/sIaHMEVBsB9cX6nS47oBBQo/MhoKLsBfT81iiKrh/ObvrZn0fRpeqmfH8uj+/K9hnOTkgE1OgzhJ38W0VAGzMX0LO9KPXCu9NN72Rbokuvze3P4fGdEdC1NAJq9DXqXfxLRUAbMxfQg+jKYXy6H115WG45ji59OU43zUY135/D4zsz/TaZ3RAJqNF3SezgXykC2ljIz2t1CejJKMvk2d7kfPN8P7oZZ5vyX2f31/zx3RHQtWQCavR9uq3/+0RAG7MW0KPoavHrjWLL2V5x5nkw2TTZX/PHd0dA1yKgzfXuE+mNsRbQg+I087gIaeUfEdDmCKg2AqrNWEDP94un7iej6YuguZln9ZP9NX78LXARaS0C2hwB1dadgB5Nz0lvlx7sXi2gAVagay6gwVZidDBtL7se0Hb31jmtH/BeA1q75n7M25iccAaqjTNQbSHfJeHzDPR4dOnm/P4aP/4WbN/pshM6AbWDgGoL+T5djwE9WnD+yZ0umyCg2giotpB3ivm7Cv/Own5yp8sGCKg2Aqot5GcVuL0P9Ebl19T5QXS5fhNSvr/mj+/O/J0u7SOgzRFQbdYCOn8nUnZ358OF38xFivUIqDYCqi3kB167BPR8P7pcuxf+aFk/CegGCKg2AqrNWkDj05lPYzoZJeehxcczpeo3JxHQ9QioNgKqzVxA49NbSSqvZ+ecWUCPI8GA2kFAtRFQbfYC2mR/LT9+hc35E1BxBFSbtYtIzfbX8uNX2Jw/ARVHQLURUF9szp+AiiOg2gioLzbnT0DFEVBt1u5Eara/lh+/wub8Cag4AqrN2L3wDffX8uNX2Jw/ARVHQLUZ+zSmhvtr+fErbM6fgIojoNpC/p1VBFQAAdVGQLUZ+0T6hvtr+fErbM6fgIojoNoIqC82509AxRFQbQTUF5vzJ6DiCKi2na6agOohoNpCHqAEdD0C6ovN+RNQcQRUGwH1xeb8Cag4AqqNgPpic/4EVBwB1UZAfbE5fwIqjoBqI6C+2Jw/ARVHQLURUF9szp+AiiOg2rodUIhhPuKYjzYC2m/MRxzz0bbbgAITu30+6o3RZTuw+ZMGXTUBxc7YPD6tLtuBzZ+UgKIfbB6fVpftwOZPSkDRDzaPT6vLdmDzJyWg6Aebx6fVZTuw+ZMSUPSDzePT6rId2PxJCSj6webxaXXZDmz+pAQUACwioADgiIACgCMCCgCOCCgAOCKgAOCIgAKAIz7OrmdanjfQKwS0Z1qe91Lnt0ZRdP0w1O63cLZ3NfQSdsHsgMLOh4D2TMvzXuZsL0q98G6g/W/hIOpDQO0OKOx8CGjPtDzvZQ6iK4fx6X505WGgBbg6P4h6EVCrAwo9HwLaMy3Pe4mTUXZqc7Z36bUwC3D1sz+LehFQqwMKPh8C2jMtz3uJo+Jf8qPoRpgFODqKoi/9tA8BNTqg8PMhoD3T8ryXOIhuZr8eG4vR0eWvmVuzE6MDCj8fAtozLc97sfP94pnhycjaa2zmmuLE8oAIKHao5XkvZvn4JKDiCCh2qOV5LzZzfNp7n0zPAmpuQAQUO9TyvBezfILTt4CaGxABxQ61PO/FLB+fBFQcAcUOtTzvJYxe5M1YXHNjhgdEQLFDLc97ifLthcbeZpgx2JTmDA+IgGKHWp73ElZvdEn1IqCGB0RAsUMtz3uJ8/3osslbrePQB+iOGB4QATXh7fiXoZfgRcvzXubU7If99COghgdEQE0goNs5vZUcntfNnd7EoQ/QnTE7IAJqAgEFUEdAN0RAAdQR0BXu5tF8O/5dGdBvvP/x3wVe1JZanjfQKwR0hbmAJv38eehFbanleQO9QkBXqAf067+IrfeTgAIeEdAVagF9/YcdeB205XkDvUJAV6gF9G4H+klAAY8I6ArVgCYIKIAZBHSFWkB/bf4S/G0CCvhEQFeoBvR3+W+Ma3neQK8Q0BW4Cg9gFQK6Qn7G+foPJ+8DfSP+/VuhF7WllucN9AoBXeGNOP6n23/9w3gS0A68kanleQO9QkBXSHqZ+MN/mb2V8w/fD72q7bQ8b6BXCOgqr/99HP/LD96YBvT2XevXkVqeN9ArBLRnWp430CsEtGdanjfQKwS0Z1qeN9ArBLRnWp430CsEtGdanjfQKwS0Z1qeN9ArBLRnWp430CsEtGdanjfQKwS0Z1qeN9ArBLRnWp430CsEtGdanjfQKwS0Z1qeN9ArBLRnWp430CsEtG9aHjjQJ60HtOXHr3iwy535s8tlE1DAIwIaHgEFjCKg4RFQwCgCGh4BBYwioOERUMAoAhoeAQWMIqDhEVDAKAIaHgEFjCKg4RFQwCgCGh4BBYwioOERUMAoAhoeAQWMIqDhEVDAKAIaHgEFjCKg4RFQwCgCGh4BBYwioOERUMAoAhoeAQWMIqDhEVDAKAIaHgEFjCKg4RFQwCgCGh4BBYxqPaAPoISAAh5xBhoeZ6CAUQQ0PAIKGEVAwyOggFEENDwCChhFQMMjoIBRBDQ8AgoYRUDDI6CAUQQ0PAIKGEVAwyOggFEENDwCChhFQMMjoIBRBDQ8AgoYRUDDI6CAUQQ0PAIKGEVAwyOggFEENDwCChhFQMMjoIBRBDQ8AgoYRUDDI6CAUQQ0PAIKGEVAwyOggFFOAT2/NYqi64eVTe8km/7ky3PfSkDXI6CAUS4BPduLUi+8O910mm+KvlT/XgK6HgEFjHIJ6EF05TA+3Y+uPCy3nO9Hlw/j87+NLr1W+14Cuh4BBYxyCOjJKDv3PNub1vK4OB09iq7WvpmArkdAAaMcAlpW8ii6UWxJTkBvLvlmAroeAQWMcgjoQVHL48np5tne7OuhFQR0PQIKGNU8oOf7xVP3k1H5Imj6u59+IYouf23uuwnoegQUMMpXQG/lV+HLJ/XJgVp4ACUEFPBoq4CWT9yP0zcwPYzP3+EqvAvOQAGjvJyBHpenngdchXdAQAGjPD2Fn9tUIqDrEVDAKC9X4SdP5qfP6ksEdD0CChjl9D7QG5VfZ05KjyPOQJsjoIBRfu5EKl/7PJhehi8Q0PUIKGCUQ0DzG98r98KfjNIPZ+IqvBsCChjl8mEipzOfx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class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a7-differenced-dependent-variable" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a7-differenced-dependent-variable">Appendix A7: differenced dependent variable</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>dcd <span class="ot">=</span> dc <span class="sc">|&gt;</span> <span class="fu">group_by</span>(cn,year,party) <span class="sc">|&gt;</span></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>   <span class="fu">mutate</span>(<span class="at">attention_others_last =</span> <span class="fu">lag</span>(attention_others),</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>          <span class="at">attention_others_diff =</span> attention_others <span class="sc">-</span> attention_others_last,</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>          <span class="at">party_share_last2 =</span> <span class="fu">lag</span>(party_share,<span class="dv">2</span>),</span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>          <span class="at">party_share_diff =</span> party_share <span class="sc">-</span> party_share_last,</span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>          <span class="at">party_share_diff2 =</span> party_share_last <span class="sc">-</span> party_share_last2)</span>
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a> out <span class="ot">=</span> <span class="fu">lapply</span>(cns,<span class="cf">function</span>(x){</span>
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>   <span class="co">#data$econ = ifelse(data$topic2 %in% c(1,103,105,107,15),1,0)</span></span>
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>   fit <span class="ot">=</span>  <span class="fu">lm</span>(party_share_diff <span class="sc">~</span> (party_share_diff2) <span class="sc">+</span>  attention_others_diff,</span>
<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a>             <span class="co">#+ scale(share) + factor(topic) </span></span>
<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a>             <span class="at">data=</span> dcd  <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x))</span>
<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a>   <span class="co">#test = coeftest(fit,vcov = vcovCL, cluster = ~party)</span></span>
<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a>   <span class="co">#tcofs = data.frame(test[c(2:4),c(1,2)])</span></span>
<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a>   <span class="co">#  cofs= data.frame(summary(test)$coeftable)[c(2,3),c(1,2)]</span></span>
<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>   <span class="co">#cofs=data.frame(vars=rownames(tcofs),tcofs,country=x) </span></span>
<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a>   <span class="co">#cofs</span></span>
<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a>   <span class="fu">names</span>(test) <span class="ot">=</span> x</span>
<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a>   fit</span>
<span id="cb10-20"><a href="#cb10-20" aria-hidden="true" tabindex="-1"></a> })</span>
<span id="cb10-21"><a href="#cb10-21" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb10-22"><a href="#cb10-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">names</span>(out) <span class="ot">=</span> cns</span>
<span id="cb10-23"><a href="#cb10-23" aria-hidden="true" tabindex="-1"></a> <span class="fu">modelsummary</span>(out,<span class="at">estimate =</span> <span class="st">&quot;{estimate}{stars}&quot;</span>,</span>
<span id="cb10-24"><a href="#cb10-24" aria-hidden="true" tabindex="-1"></a>              <span class="at">statistic =</span> <span class="cn">NULL</span>,</span>
<span id="cb10-25"><a href="#cb10-25" aria-hidden="true" tabindex="-1"></a>              <span class="co">#coef_omit = c(-1,-2,-3,),</span></span>
<span id="cb10-26"><a href="#cb10-26" aria-hidden="true" tabindex="-1"></a>              <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;(Intercept)&quot;</span> <span class="ot">=</span> <span class="st">&quot;Constant&quot;</span>,</span>
<span id="cb10-27"><a href="#cb10-27" aria-hidden="true" tabindex="-1"></a>                              <span class="st">&quot;party_share_diff2&quot;</span><span class="ot">=</span><span class="st">&quot;Stability (diff.)&quot;</span>,</span>
<span id="cb10-28"><a href="#cb10-28" aria-hidden="true" tabindex="-1"></a>                              <span class="st">&quot;attention_others_diff&quot;</span><span class="ot">=</span> <span class="st">&quot;Overlap (diff.)&quot;</span>),</span>
<span id="cb10-29"><a href="#cb10-29" aria-hidden="true" tabindex="-1"></a>              <span class="at">vcov =</span> vcovCL, <span class="at">cluster =</span> <span class="sc">~</span>party,</span>
<span id="cb10-30"><a href="#cb10-30" aria-hidden="true" tabindex="-1"></a>              <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|AIC|BIC&#39;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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    <div class="container">
      <table class="table table-borderless" id="tinytable_mo175xbxufjgvyvhp3dl" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">be</th>
                <th scope="col">de</th>
                <th scope="col">fr</th>
                <th scope="col">uk</th>
                <th scope="col">dk</th>
                <th scope="col">nl</th>
                <th scope="col">it</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant         </td>
                  <td>0.003**  </td>
                  <td>0.001    </td>
                  <td>0.006**  </td>
                  <td>0.001   </td>
                  <td>0.001    </td>
                  <td>0.004**  </td>
                  <td>0.016*** </td>
                </tr>
                <tr>
                  <td>Stability (diff.)</td>
                  <td>-0.233***</td>
                  <td>-0.194***</td>
                  <td>-0.397***</td>
                  <td>-0.194**</td>
                  <td>-0.279***</td>
                  <td>-0.224***</td>
                  <td>-0.116***</td>
                </tr>
                <tr>
                  <td>Overlap (diff.)  </td>
                  <td>0.241*** </td>
                  <td>0.116**  </td>
                  <td>0.076*   </td>
                  <td>0.096*  </td>
                  <td>0.089*** </td>
                  <td>0.136*** </td>
                  <td>0.221*** </td>
                </tr>
                <tr>
                  <td>Num.Obs.         </td>
                  <td>1880     </td>
                  <td>779      </td>
                  <td>702      </td>
                  <td>418     </td>
                  <td>2478     </td>
                  <td>1496     </td>
                  <td>792      </td>
                </tr>
                <tr>
                  <td>R2               </td>
                  <td>0.133    </td>
                  <td>0.110    </td>
                  <td>0.218    </td>
                  <td>0.096   </td>
                  <td>0.139    </td>
                  <td>0.126    </td>
                  <td>0.056    </td>
                </tr>
                <tr>
                  <td>R2 Adj.          </td>
                  <td>0.132    </td>
                  <td>0.108    </td>
                  <td>0.216    </td>
                  <td>0.092   </td>
                  <td>0.139    </td>
                  <td>0.125    </td>
                  <td>0.054    </td>
                </tr>
                <tr>
                  <td>Log.Lik.         </td>
                  <td>3551.148 </td>
                  <td>1774.125 </td>
                  <td>1239.775 </td>
                  <td>951.305 </td>
                  <td>2849.400 </td>
                  <td>3008.582 </td>
                  <td>1296.994 </td>
                </tr>
                <tr>
                  <td>RMSE             </td>
                  <td>0.04     </td>
                  <td>0.02     </td>
                  <td>0.04     </td>
                  <td>0.02    </td>
                  <td>0.08     </td>
                  <td>0.03     </td>
                  <td>0.05     </td>
                </tr>
                <tr>
                  <td>Std.Errors       </td>
                  <td>Custom   </td>
                  <td>Custom   </td>
                  <td>Custom   </td>
                  <td>Custom  </td>
                  <td>Custom   </td>
                  <td>Custom   </td>
                  <td>Custom   </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a> test <span class="ot">=</span> <span class="fu">do.call</span>(rbind,out)</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> test<span class="sc">$</span>var2 <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Stability (fixed)&quot;</span>,<span class="st">&quot;Overlap (diff)&quot;</span>, <span class="st">&quot;Overlap (fixed)&quot;</span>)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> test<span class="sc">$</span>cn <span class="ot">=</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;Italy&quot;</span>,</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>                 <span class="st">&quot;Netherlands&quot;</span>,<span class="st">&quot;United Kingdom&quot;</span>),<span class="at">each=</span><span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="appendix-a8-topic-and-country" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a8-topic-and-country">Appendix A8: topic and country</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>tcoeffs<span class="ot">&lt;-</span><span class="cf">function</span>(<span class="at">df=</span>data,<span class="at">topic =</span> topic,<span class="at">cn=</span>cn){</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>  df2 <span class="ot">=</span> df <span class="co">#%&gt;% filter(cn==&quot;BE&quot;)</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  reg<span class="ot">&lt;-</span><span class="cf">function</span>(x){ </span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>    fit <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others, </span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>                                <span class="at">data=</span>df2[df2<span class="sc">$</span>topic<span class="sc">==</span>x,])</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>   <span class="co"># test = coeftest(fit,vcov = vcovCL, cluster = ~year_party)</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>  <span class="co">#  tcofs = data.frame(test[c(2,3),c(1,2)])</span></span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>    tcofs <span class="ot">=</span>  <span class="fu">data.frame</span>(<span class="fu">summary</span>(fit)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)])</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>    tcofs<span class="sc">$</span>topic <span class="ot">=</span> x</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>    tcofs</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>  }</span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>  df2 <span class="ot">=</span> df2 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(topic) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">no =</span><span class="fu">n</span>()) <span class="sc">%&gt;%</span> </span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">filter</span>(no<span class="sc">&gt;</span><span class="dv">20</span>,<span class="sc">!</span><span class="fu">is.na</span>(party_share_last),topic<span class="sc">!=</span><span class="dv">99</span>,topic <span class="sc">&gt;=</span> <span class="dv">1</span>)</span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>  tops<span class="ot">&lt;-</span><span class="fu">unique</span>(df2<span class="sc">$</span>topic)</span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>  tops <span class="ot">=</span> tops[tops<span class="sc">!=</span><span class="dv">23</span>]</span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a> <span class="co"># tops = tops[tops&lt;25]</span></span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>  testFunction <span class="ot">&lt;-</span> <span class="cf">function</span> (x) {</span>
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>    <span class="fu">return</span>(<span class="fu">tryCatch</span>(<span class="fu">reg</span>(x), <span class="at">error=</span><span class="cf">function</span>(e) <span class="cn">NULL</span>))</span>
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a>  }</span>
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>  mods<span class="ot">&lt;-</span><span class="fu">lapply</span>(tops,testFunction)</span>
<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>  <span class="co">#coeffs&lt;-lapply(mods,coeftable)</span></span>
<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>  <span class="co">#mods2 = do.call(rbind,mods)</span></span>
<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>  co2<span class="ot">&lt;-</span><span class="fu">bind_rows</span>(<span class="fu">lapply</span>(mods,data.frame))</span>
<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>  co2<span class="sc">$</span>type<span class="ot">&lt;-</span><span class="fu">rep</span>(<span class="fu">c</span>(<span class="st">&quot;stability&quot;</span>,<span class="st">&quot;overlap&quot;</span>),<span class="fu">length</span>(<span class="fu">nrow</span>(co2)))</span>
<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a>  <span class="co">#co2$topic&lt;-rep(tops,each=2)</span></span>
<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>  co2</span>
<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a>bet <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;be&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;Belgium&quot;</span>)</span>
<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a>dkt <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;dk&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;Denmark&quot;</span>)</span>
<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a>de <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn<span class="sc">==</span><span class="st">&quot;de&quot;</span>)</span>
<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a>det <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(de),<span class="at">cn=</span><span class="st">&quot;Germany&quot;</span>)</span>
<span id="cb12-36"><a href="#cb12-36" aria-hidden="true" tabindex="-1"></a>frt <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;fr&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;France&quot;</span>)</span>
<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a>itt <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;it&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;Italy&quot;</span>)</span>
<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a>nlt <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;nl&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;Netherlands&quot;</span>)</span>
<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a>ukt <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">tcoeffs</span>(dc[dc<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;uk&quot;</span>,]),<span class="at">cn=</span><span class="st">&quot;United Kingdom&quot;</span>)</span>
<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a>test <span class="ot">=</span> <span class="fu">bind_rows</span>(bet,dkt,det,frt,itt,nlt,ukt)</span>
<span id="cb12-43"><a href="#cb12-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-44"><a href="#cb12-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-45"><a href="#cb12-45" aria-hidden="true" tabindex="-1"></a>test<span class="sc">$</span>topic2 <span class="ot">=</span> <span class="fu">factor</span>(test<span class="sc">$</span>topic)</span>
<span id="cb12-46"><a href="#cb12-46" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(test<span class="sc">$</span>topic2) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Economy&quot;</span>,<span class="st">&quot;Rights&quot;</span>,<span class="st">&quot;Health&quot;</span>,<span class="st">&quot;Agriculture&quot;</span>,<span class="st">&quot;Labour&quot;</span>,</span>
<span id="cb12-47"><a href="#cb12-47" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;Education&quot;</span>,<span class="st">&quot;Environment&quot;</span>,<span class="st">&quot;Energy&quot;</span>,</span>
<span id="cb12-48"><a href="#cb12-48" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;Immigration&quot;</span>,<span class="st">&quot;Transport&quot;</span>,<span class="st">&quot;Police &amp; justice&quot;</span>,</span>
<span id="cb12-49"><a href="#cb12-49" aria-hidden="true" tabindex="-1"></a>                         <span class="st">&quot;Social policy&quot;</span>,<span class="st">&quot;Housing&quot;</span>,<span class="st">&quot;Econ. regulation&quot;</span>,</span>
<span id="cb12-50"><a href="#cb12-50" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;Defense&quot;</span>,<span class="st">&quot;Science &amp; tech.&quot;</span>,<span class="st">&quot;Trade&quot;</span>,<span class="st">&quot;Foreign policy&quot;</span>,</span>
<span id="cb12-51"><a href="#cb12-51" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;State &amp; admin.&quot;</span>,<span class="st">&quot;Domain&quot;</span>,<span class="st">&quot;Culture&quot;</span>,<span class="st">&quot;Reunification&quot;</span>)</span>
<span id="cb12-52"><a href="#cb12-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-53"><a href="#cb12-53" aria-hidden="true" tabindex="-1"></a><span class="co">#png(&quot;art/country_topics.png&quot;)</span></span>
<span id="cb12-54"><a href="#cb12-54" aria-hidden="true" tabindex="-1"></a>test <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(topic2),topic<span class="sc">!=</span><span class="dv">21</span>,<span class="sc">!</span><span class="fu">is.infinite</span>(Std..Error)) <span class="sc">%&gt;%</span></span>
<span id="cb12-55"><a href="#cb12-55" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(topic2,Estimate,<span class="at">color =</span> type)) <span class="sc">+</span> </span>
<span id="cb12-56"><a href="#cb12-56" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymax =</span> Estimate <span class="sc">+</span> Std..Error, </span>
<span id="cb12-57"><a href="#cb12-57" aria-hidden="true" tabindex="-1"></a>                      <span class="at">ymin =</span> Estimate <span class="sc">-</span> Std..Error),</span>
<span id="cb12-58"><a href="#cb12-58" aria-hidden="true" tabindex="-1"></a>                  <span class="at">position=</span> <span class="fu">position_dodge</span>(<span class="at">width=</span>.<span class="dv">5</span>)) <span class="sc">+</span></span>
<span id="cb12-59"><a href="#cb12-59" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span><span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb12-60"><a href="#cb12-60" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb12-61"><a href="#cb12-61" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;black&quot;</span>,<span class="st">&quot;gray&quot;</span>)) <span class="sc">+</span></span>
<span id="cb12-62"><a href="#cb12-62" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>(),<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb12-63"><a href="#cb12-63" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(.<span class="sc">~</span>cn,<span class="at">nrow =</span> <span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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gKo+Ap+/jLMQpASLXkpjJ/DJ2+CEhIjQDFfjJuaaP1iZHlhauLUpNmMWrzEn/Euyk8N0Gb7+7I4vpQh59hkZvXiyq3zsnnLDtkpTm+aeKy/ynR2VOzdXotdoa88uVHs/TafCr1yt5RD+OrtsxeKwr5GqVGaJa+E8nPw8mFQQiIGqGsrSsYANbJskcFLUAKoG6CuuhSQmlF6oOD0ZE3Le3Je9LB5CwvZyfZvXtzQ9x41Xy0ZHpm+LAD6MrdwvFTzphKgV51nOKXSLHklnJ9DN7AFJSRegDp9kS9AzSwTQIPMtAO0pV1NDdCnJ8XerQ/qH9dyJnTvbqn2w4u30JCdbOfnkb4UZHx173Z5uSwEQCsD1ffZWxd83lQCtDg8d+0AZdlPXS6AdjSTi1vG8AVQOtXFyrKFhjF/Wzp+aVG7lzrVJcMvY6zCi82b114TENWOW4ojlvAAkbl0vi4ELxtpXxU/iGUmcU7pyZHcY1/9IAF6AKLXW5qoWDYpnJ/DAhlOk9KNaARfACVTXZAsT8jPdPzSqo78bKlLhl/G2Qf6SOx/f4mBrJnQ/Oi9V6v3FUDRkJ1sRye2iUl9VQUJWTUn4+VbEKCH9SMOpTn2qOUCqFD/1EQ6VG0z4/LFaKmJwy+O4oYKH8DPeAjvW4V3uzY0NaOdRHqfbaZng3MJ0PpMUQNQO+Ic2wH1YMl+fvcrzRoQ+Co/f1RKC06ANrFFHEqt5A0qEkClmsVnAigEqHw1ZWrCTETZjNIAaKWLE4YyAVC2YLR37eUHzRAeC9m5kmQ9+D6IpgS/quIzqVX4cpYAxddNBgYyDEpIlAAtvb7IBqCmGSzLGkAnT02qzcjbTX+MOjY4NcMBCkJxcqip7uahjBTfANQaqstRfUXMq00QJe2rRg90rgBtW3numZo4QeERaBFOX2QLUHSxGAB0+tQk24x80xxbX4UHoTgbgCpaVt1OMIQ3l8nVU2sx0tff5F815kBnC9AJ7mMNSkikAC0JoFrviqIxoXJFYzLNbBegFbnUkHxZx0cCaDxuVuHNkJ0VU9WCe9M71b+qr8LPFaD4JOjg1EQKCrf0FkEA5aIeqEtm1ACXmS0DlO0DlUE8xc7Ng3M5Dmd7NgVAGTKRkJ08sqdYNKoJqn+Vb1q6vFfMGaDNn0+tnIenJlJQuGVNaiF1Pl+AOg/MqEUkOsqpKyR8bAQnkZpVc37GSMTsXIt3Du6rI0hscG+H7JSHjvZu3WsOKGlfrbgITiK1AvTJERrYKdmSZ4ITOLCc5wtQ4BG7zmcMUMeR7XoVnk4i6QoJHyvMeMd106/CX/LD60AV29jW0Cu3ZedThPHUQnbK7fTsMR79816zDV77ann2AjgLXzKA/vznnn+r/FfFn5sHQPUlRFDOswWo5hGrzucMUDRo0AIA1EPQ+QJUb0G4GQ8/N7SNqQ4K6j2U3ig0lCcivjTf6fuplTwwo5GClX9dyjME6GM13vK0iBwBClq21cgXCz8gRkxNSs2IT3LxR1oc9Bh8oUdqRgRoEDfHkNrbFK6USt4wow1Vd7qtliQHigAztSPmA1Bf74gAigq6LBCgPVNDAJVPRFLyphmTn512jCcGihAzwBGOFpEdQL3zc+ExQecEUOiyNgfFClB2saaM0bkujs+Oiv3XtYCgYgjO1tf3HzrCesr4nzIuaGMQDuHlfGmzOQpdjE+m5C0zFj+bVjRHgGqOmAdA0bJXsujgJuiMAIrVktQAugaxP9fFi2IZHgYE5QAU5+P3voSH9VTxPw8NgwCgYo2evZTdUmS7fplOydtmbH7WrWiGADUcMQeA4mWvRABFhNaSxABaofKlD1j/UuwMFb1DLSAoB+CKhbJju6DQsJ7VOwcP5TvQYAPQ6t3PVt/hBtUavTm8nypI1maEAbSnqQzcgjaNgTbLuP3SVvYWP8f6xZH7xaMWgA60bvhlglV4GPtTnk0C5zHLmp3HqrsoInzaYT2Xoje6Ejs+G4MNQFcqan0ddNS+PGmUMtmebH72JWj6brEcMQYw4q4u7WU/ET8j94tHaC0Zz0GGX6YBaD2QlqfjD0R0EBgQlAFQRQZd4mE9JUsZLzWDNUC18broey7RuPSJjD2gGVD6Vo1oNCw1MQ1VQxcFrKZhOiHdIbynnD0KG8DnPITv5qGRUzPNEL6OvMSpVgMU7hRlAFzDLql9IlPCkIFXM1gDVA/wtATRmwxFWvJOVVAApe+pG8NSQwDtnJJQE33MdCSn5gMCqN9DyQFU/iABeiheDwEoMAgACnHJrDmuP4605J3STyI5sDm/RSSsaUyRmoj84ih7qAWdRNLkB+jYqdlMD/RQe5NLAFRtPRqjB/r05OAcH8EnUvKNrKPOWBuaH0CR9dVJUhOTX1r5SWfhTVku885wRAlQcw70UHuTC5kDtQF6rCwHzIFWz+99/RTfYp9IydcyojERQKUsgE6Tmpj8EgBQisaky3aZb4IjSoCaq/BqpA4CggIAPj1xAZQ/vxIXGHtW4eU/bL8pHtIukZKvBcPIlK5GRADtOSRLyi8hAC2Nzvj2/BJFM7JricMlY6Rm0n2gH90B18IbAUHVPtDXm32gNkCbnaHQoLkP9JHcO8omWfEII4mUfC0j6hB+GmXuAHV0KzIDaEusSvUM+GGbfomjGWHVZKrUbOYk0qH2Jg8IipxEsgH6ZXXOqOUkkviKFtheUyolr2QGQUDPQ88coK6JrdwA2nIWXj7SvN6qXyJpRu1/ZkdLzWbOwh/Wb9YBQeFZeGQOdAXDix7rBs2z8MVfkuZXzdV0upIpeSlj5luPAzpeauICRRBLSoSfeuPIDqAt0ZiYzLsrtuaXWJpR65/Z0VIz5rXGvYWsndsAdarhszNGaKiTvLV0sxVosTCagpW2GQKUm7H5qTWO/ADaWi+BGeYMfOA6K4CCqLGNNyZJzVYBKnutyIXHXMviV0Li1tUArY86WQpzUutf+q0CdILURAeKEDNY5yJ3gIabAXve5g1QpoXujUlSs1WAPj3h98ctHSPvrgA9c14rF+Sk9rmmDVaghakpUpMiKPBZUOCeWQNU2zO8Bb9EBdCF6Y0pUrPdIfyZvFMO7zgCgDYhRfn1S1dkjNDqPXlfUln+IbN07SFqKcRJO1COhwLMtD5CAB1iRislAig0Uw9YDf+Ml5qUAIp7Y/zUbHkOlOFw7yWcejpAZUjReyDkE9vnpG7sLJ/8crPsbynASTu68IfazRBAJzVjlBIBFJixiCH9M15qUgOo7Y3xUxPFIpJLzVl3FVJ0zQKIsp6r2F+6d5tNADCAarFGNfULgjU06NVQWQAdbrKXWyKTE6D9TfarLpHK8k9/U+n7xQLoKFYNvyQDUDHMl3udxBEm8UP1GpxUshfiw9yyY2oUZw/Q6PzMAaBWKY3gnl7VJVKNWYvT98s0bdrwSzIAbdaZPnrv1RsFD13XxGnWY41aUrm2XNpHvhTTKvyUZuyyQNwztyF8aLWdwxC+TyueyxBecpGd++RqYn+uOUC9t9KnBlD7MsFeZrICRUkARRVabQmgeCueC0DFCJ3Fvd+79vIDNoRXZ5/WPL5IEEDdCsLkxiqQfRJpktQkBQomF0BHTk2CflH/umvxHACqK6hNzwugbL5TBrfXe6CuvfhSqa3CW2fhp0lNgqBo5eesAeqL3TQ/gIZFspoVQBUnqw6nOQeKR1KWShKg3iiGo6QmQVBgAB09NQn6pX5BAG1EAHUBdA0C4KlVeBBr1FKaAPVFMXR/0iE1CYLCAqj2OV+QH56aqP2ClTwBFBXujc5z5hkBVAzhL++LACP8OmQWz+nQiDVqKcRJ0/+xCjfTdltcS980NDVRgwKT/69coFeS9guex8YVbv/MHqDiPcuBswIo63oyHdyXt883J5FgrFFLQU5q5Wc0AG2bHQ1NTcSgwAXC2Xn42UrQdP3iyCPwhdM/cweoeMt24LYAevnmC4WK7anJGVMuUBc3WYhQ/lIC9PLN63XAEBb/88ptNVI/e6HYu/1Pi1/g32tijVoKc1IbP2MBaOv6fGhq4gWFQ8wvAfzMtmfuyiN0hss/swSo6Q3EgVsC6BPZ4Sv2zV1DAwEqDGtr6o67ipWW7b8w1Ek+enYwM1JqlIxULRaudtQ1NdGCwm3G+Tcu3Cvp+sWZR2jG1QeYIUC5GegN9tp04HYAennKpxxZh9ALt+5aFofnYl2o1uYAGqMZq0cxY4B6xggEUC63h+YK0Ea1byIAaL3Y3bIBs7OEPf1A5qwB6pvFaWNFqqBwyTNL3cEryfrFncfajMdDswco8A104HYA2lz9tmzuJrpyW3wkrzA6UuE5l8UrZy+oC5LAk9pDUuJQO0SmOKV5aD7dmKlS8KT64SX8OiSudEvengefL0BtXzTKHKBW9vSMKjM+D80doJpvgPe21QPVT0yumsuLBECfgFs5l8XV5pAleFJ7qLF0cH5xA2xGqgGqPQ3MLHms0MJ1oRxTsiW/s2O1idkCFPFFIwJo2eKhmQPU8M22Afr0pNi79UH9o7if/YwjkgNUC89p3O9eP4nG8KzevHKkD8pFf1R7GpqpzB88dO0AZdlPWEaxs7esFhRqK3W3IL5o1NsrZuMYOdFjqjWPXg91VTp+CRIK0D6GDL/0XYUXUZGuvSYgulIR4l+RANXCc8o7j5bgA/kkFsNzXYigyfCdw9J4GpqB5hGNXRKb1I4p9uY4pJgszVMJ9UWjvl5JCRTd+DmQoAn5JUCWb/rycyyAlpePRGA5NvWonURngNPDcy7VVe/6mXU0huflHfuOJA5Q7WntFwLzLiU19rDKGv3jqQ3heqYmvqGqR5Yf9I/DvZKuX+w8ttUV4K0ZDuHbWhFWj7qmZtBJpPfZZvpqFK0txQuAwuhysm+4MqImYTE82faoB0v287tfqdeWJEDB09ovBOZdSqrkCaCoCKAE0I5mogdopYuTCl/NkctSAFQPz6kBtHkSi+G5kqw8+D7oY67lYXcIUPALcwNo2RbfMZgUqYICVQtA6SRSi4dmCVDw0gboKKnpB1DQA2Q7Qq0eqL471NEDRbaQynH6k6PiKlhUlwAFT2fdAy1bo+vk3tPC5ecnnYWnVXhT6AlXg5/bASiYg2QAReZA4ZI4IJw+B2qtm4NwdeAzNYTXtjbBOdC5AXSe0ZjaAErRmAigurwAHSk1vTfSqzH0EtyJuRKvj43wnJBw+pNmDM8KjOrWTdDFVKvw4GloJluANtM0dpMI4UTKoEDUCtC5xgNltYS/IIDqsgCKTXxuB6BsH+jtc3Y4qBBHL9m2zEdH9TYmLTwnJBx8EonhWXU9b8s9Ug1BBWC1p6GZTAFa9zX6t4ikQWGrlZ9jpSYpv4gqgqwpjZ+aFJsR+AG0qPFSM2wfqHkwSPQUGc9geE6NcOBJLIbnPWn11r3mgBI7glQhVHsamNH5jB7NT7DkwWxX7xaRFShK4zTzhKlJyS+LGqAGQSdITYLNCMgxf7wlgJaXj25WALuijiM5zsLfZS/1LiI8C/9vPycxfLU+1sQ+3mPH5u9VX3r3iJ/WrP45ODcifmpn4Wvz2QBUW2/t2yJyAgUX4KeToPMC6AIAVCPoFKlJrxmBH7QWNV5qthuRHuxN2rttf7yuT80PVGolb545sdoDayLzA6j8W+JfK2JmvGP8nPwC/8RKj7jyPkOAQjNGizLM+CeFYgaoXIt6/06BHGVfiUuMhyu5kncWN1f7RGBYatIBhZDfK7WZVvfk4xd9jsdbHwigToC2+C8FgMJ1/UZDbweplVrJL0xpn4YspQSlJh1QcPm9Uptpd082fjH4OfA6hZwB6q477ZehpQFQtbWzCfq5rpeJXLFF2fVJxVVnbNFGqZW8FxVwpdVffNmAQigMoAHuycMvygtahre8uBZXM2rMWFUHALS1wqQBULnGBIJ+1gB1xRZV9zLxtSkstmit1Ereh4qdndYSD01NGqCoFQTQEPfk4ReMnxVBJ0xNas0oCKDtFSYRgPKYTHqIUDGEd8UWrfqsnz0vy0d84R2NLco1clysjcgqbvCZUeDhRpN3i88rtTq7J+XqggB0NNsp+8WWu+509Z/hl5gAWr3WQ4Sq/+OxRY0T+FhsUaYpi2UyBfOzQ4tJ3y3d+dm9QYye6Ell8XM8gibtF1uB/Gz3n+GXuABqhAjlNPTFFt1/IL+MxhZtlNzYwx5vWAVtqE9q0hiqNnKNwhqFOSZtv8DMIQB1ZX7WQ3i87gS2pWSG8AygWoQ7CVBHaDxxGmn/NQZMLLYoUGolbwb9LAmgTAvdL8gTBFACaCMzeETeABVx8WyAOmOLliVbj2dnO8/R2KJA6ZW8s6sVRM7A1MQNCkSaXxxPzOsPiw3QCVOTXjMCPzhaVID/EgGoGS60rAHqiC3KdPntm/ywUsv19AmWvJMU4fzMCRRCEKDOR9rdk5NfTIJOmZoEmxFQWI+ka2qiAWjVh3zFDBGqhvB4bFGpy/tmRFJbKZa8ixQEUB8/ZwfQ4Powe4A6Y6nmAdB7hVg2giFC1So8HluUL9uXct3Iji0KlWbJ46SYN0ClV3yPEECnSk2azQioX3tKAKCX798U8T/1EKH1PlA0tii7Oql6/+JUXplkxhYFSrPkQ8bwg1KTFCiY4NSG+5lW9+TlFy3DdBIJKODCuNJYhuucmm0DtInGJMBnBP08Nt8zgjNz7b9lftFUkiUfMgs6LDVpgaIMWUQqZ3UWXkjj53bD/EXYjPxiDhs0YOkBUMUtJt/EY4BqgF69pQbeMOjnqvg13kXVYov+FYbIf178gnr2ivyqFi3UUIol71yHn280prJ1GxM30+qe3Pyi8dMX5m9galJsRn4Jhw0ZsGwXoK3S7i/mWoOw98FKreQrM4uFh6BNPFD/tFdmoNCvOXFFYypnFA9USguTapzzHjE1CTYjv2q3eWc/xgcotk4zjVwA7ah4iizYjB+g0sys5vpKO37GRKlJ1S9GZdHdRAA1ZXTcCaBuxVJk4WYWpjAzgyZvghISFyhMfuJrpsNTk6hfjMpiuIkAasiYOvZMfkwNUHjL0cWdQkXr1AmahiEAACAASURBVEJ3Aq2L47OjYv91PYTn5b2i2H8oA4bUC0USoPL+pXN5ZPOwuXip9bdxRVJkHcyEAHRnx4+STEHRku3ZAdSqKgv0hk4CqC7NP1sFqLwgEy6YW6E7odbFi+KaTRjCU5Bx70soQOVVnWLrEgBoyG8TLmjNVGwlHwDQnR2zjXRNTVqgqG+g9GebAMpri+UmAqgmwz9bBKi4ov1MhTh+6QPWmWSbOkHoTk3rQmx210J4roq9u/yuZASga/ZZWf0KZhUsIgX9tnLSIFmTyWoS9iNGFWg1mb5bMIAONppBdcEqy2A3ZeAXn1CAhnzR8MuwVXhxXIhPSi4ZudQJSx50DoTu1LQWe+a1EJ7yKGZlGgGo/Fk8AwAa9NtSLfmu/OwayHCKNE8um5/DCZpDdWnlZw835eAXtyz/hPJzdIDCU+j1a3nqsg7dqUmettRCeKpe7RKfAy3Lj9579UahAzTst4nSb81UdGMPzwjeKnygTqlJa6gqdoG2Znt2Q/jSGsWH1Y7uqUmwGeFCXOMewU88hIdxkOrXsOPoBiiIQLcGvVEboGxor7adNgAN+21c2y+ybmb0aEwEUCFezwmgiIzwhwRQIWeG4wJos2xTv24B6KF4JhSgrMu7d+3lB6cmQEN+G1eKJe/kZ/1Ma+vIERSt2SaAindMN81wEcnzFwMDaK/UbKUHemh+sQ6ihAG06qoeyi7rjHqgnrPw9UNtvYssQdGW7dkDVL1luIkAanwWys+JAeqbA/UCVAvh6ZsDVaDkEUM9c6C5AbQt8NAsAdoeQHieALUqCwG0I0B7pmb4NqYVWL0x1sW9ANVCeEoYVoysV9dZx1MD6JrvcXKvwmcHUDx+Yf3BPAHa+ndjpgDVK4tVP+YNUKQdtbeesNSMtQ/0Ed+AKXZmfnRH7sz0A1QL4bliX5H7QCtS3mZhPrUh/OX9Qn5Yh1oO+m3CBa2ZirTkcWGrBX1SkyIo6CQSIlfodeim+QIUH8kF83MCgBZwH5M6icTBpZ8NgqE7mwnP+jQ7DOEJTyKJ189/p9lIL37XfRUE9OAcO4k0G4Bi6629UpMgKNr/bBBA7foxUmrSbEautYRQfk4OUPdZ+BaAirPwf+1zfDGdnYX/C78lToRe3mHn4tf1Nib5C0Tn890jDlD+oOO32S5ozVSMJe8QtmOlX2oSBEX7n43ZA9SuH2OlJslmpLnD+DiIn6MDdGTpF8uFRhjtFhB0oiKznb+BCrRYmC2kl5mwhEQGioWK3Cj+P1VqkvMLNyNro1k/RkxNagBlZgx36A8wl2Fm9IadAkDFBXEVQL23E/fWJEWG/QHbPED7mglLSGSgaPqe7rzPFKC6Z5D6QQDFq4xtxmzYKQC0Qufhefmnv4XeqTlcUxQZOoUyfQVamOpnJjAhcYECzn468z5PgJqesevHPAHa3lwsM1bDTgGg5ZmYU/25v8feck1tXrCwoFdvizeOjRigIKCopQmKbGfHcnQfM51TM2eAaj4ngIJHLM/YVYQAGgZQu2EnAVAeM/nnf+l5cMEmspTfrPdLgDYxQOEyvqXxi2xnx3Z0DzPdUzNjgBo+J4DWsj1DAC37ARRp2GkAtHlphPmsAVoh8rPVAP8RX9CXAG1igMKAopo2Eg5rml+CyaoRwd/chFsmlY2JMaxupLpMLB9A+9rMwS89mktrwzb8EgNAm11RMrYyfsBIOzwvAVrHANUCimqaolhGCLfYW335mTxALZ+Pws8cQGF7Zjg/c/AL0zB+Ig3b8Et8AHUeca8+2H+gviUBWp8i1Q7TWxo0aLB8GibP7xqSmlmuwuMeXqBunscQ3l/3QP1o1pKHpybBIXynVXhHO05sCO8OssSPPO2/hoZt0uLhWUoNoL5vE0AJoATQIDNWVMiZANQZ5pMtubOjn+cbBShmxoXLjZ9E8v+i9EEBFPgnah4A1eQB6KipSRGgrc3lsfEVu5IlB1BPmM/Lb98UMUVsgIKAopZSW4UPPgvf8ovyAkUQP+cIUPcq/LipSRKgbc0lu1X4tjCfl/fZ+yZAJ5wDRc1sE6BtgUKbX5QXKAigLrkAOnJq0gRoS3PJDqD6KjyIDSqOe8pweiZAtYCillIFqLPU2QfDM5UYKDwABY6aJ0CNGFVIxZkvQGuEhpjJAKBamE8QG7RiJIsfenEqwKoDVAsoammCIsPb8jRjj75mcgOFi5+ao+YHUAZPuWDk6ZrPGaC+pmSasStZagDVTiKB2KD1SaT9txCAbvgk0rRn4YPuRJofQB0hHHVHzQ6gixqgCw8/Zw1QX1OyzFiVbEsAvXyTrZlfu+t/alX8mg5Q9h84Cw9ig8r4oVdu/ZvqER4QVJsgFWfhNzUHWk4ajSnkVs55AxS8aThqbgBdAID6ghvOGKDepmSbMSvZdgBah13ef8v3mLVqDq9Jdog/4ggIukQDik5UZPbf+8kO8SK/boYAffzY8jn7WXPUzACK1JMSq5lzBKg0Y7lIn3VDvthhWDkVQOWEJQsmj4WIdysUoIbkCpN23LNRgiWPlrrW+ZonQHXVTiGAAoBi/XQCqJI5QB+WmqkAqvZlupDmVE+AioCil0s8oGh6Jb8wZU//EUDhdFXtqHkB1KonpWumeLYAbWlIkQK02bgpZyXhvCa4RUmOxFkwu+LKrXONjtVw/MmNYu+WQGJjAA7ha0v/WkwY7P0jrMebXsl7+DnSn84gC9GAAjWjOWWOALXwyQiKVJWRUpNeMyp9/Ay9bG9LPVC9lwhX1tU9nselAui9oo70qQH05SPsvk8A0MbS07/9c9WnLz04xSZB0yt5X7GP86czyEIkoMDNGE4hgDqrykipSa8ZlaaPbO9EClB2vdGtD+oftRif4ib5Mxm7jt/1vne3ZFHpq081gLJ51DNw47ww0AAUWlrKnfTm8H6SIFmTy98qepnMwS2aHADtqDJpv1j8HC1Sbdp+ARq5IRl+mWwVnu1pL4prrwmIIqeLBPHgfk5+mkgDKH+5Ekc3GwMNQKElsdNpbY3gRy+QzchX7P2aRRZuAbKc0oufqYOihZ/9CZq4X4DGbUiGXybcB/qII7R46bzUz7dfwlF2vRvpo/devVGYAOWfsXc0AzVANUviixvdxjSlGT9AgQakJpmhqmkG9wR31cxX4X01ZdgF6K0pCbGwFTNB7umbmmlPIr3PNtObEZbsyPKyu1pYAOUwvDzde90wIAGqr/Av9W9DpVbyVXkSQBGp3BJAa8n6QQBFZAYTSQ+glS5OOOlAjE8Ncmoqs9i79vIDawgPAGoaWEGzXGsQFtRQiiXvBeg4qUkJFFxaPUcBuqHFkoj8ogHUEzV1hotI9kkkxDtxLiKBvqEYdPt6oCzWEtuqZM+BduqBPj05OMdH8GmWPAAoGoho7gDFVuHHSU1afgEAZT+6elYzBqiboMNTMxFAweykNu2JzoEqDj49QedA2SGjgDnQ6vm9r5/iu/DTLPmGnwRQoTaAjpSaxPxSA5T/RACthURjSgWgFd8UyuDCu/bDSoWlUwBdF/qwXB4rWpkGrFV4+c+6ePEIPzeaasm7WkVgVyszULQA1DRTO69zahL0S51VkxJjpibVZlRKgpZYnYkUoGwf6O1zfn5Ibv1sYnyK3ZuPjtQ2JjGEv7xfWABtrn2HBsx9oMKSuN8TjTCScsnX7+h9rcUMAypbfkG7WtIM6L53Tk26frF6WqOmJtlm5BrLjZCaifeB1oePsJNIh2W9kZ7r4L7qXgotiy/XJ5b8J5HkhUmnhePcfbIlD98y+Nl6oUfOoKh/dvW04ARy59Qk6xdr3XHc1KTajLTKYNaZLQK0jlh35ZYZwaNi3Hcf/R3+kTyO5D4Lf3GzKP4s/1lOcP4zydBl8UrV/RTP1QYu37xePfJPir9qnIUXxtBQIumWvP6ewc92gmYLCvAG3tNaLBYeN+XqFy3XBj9nDFCjMhh1JgaAFha4fCE7USv6cnoNUPvxddNxteT8hamVPG5GpyfWSDqmJjlQ6GZ47i0XsEd0L9kxdPP0izfXLVUlKDVpNiPbLY65YZeLJgSo5Oajox5hkZWWxeE5vzQO+cQJUFRsvxP+SZolj8jCpzlQ65SaREEh5Mo9ARTJdWtVCUrN9ut/HzNtlUH+63bR9AC1Yi91AKhYgm9MAXUF6JnzozRL3hbOT/c1OHmCgsuZ+8d4kMwuqUnTL55ct1eVoNRsvf73MdNaGcQ/HhdtAKCSly0hO0t5q9G1h8CI6DZqWORfXhbHDKBihxKbJi2u3lZXxx1ipv+QBy+BphslWfKIHPx0XsSYJSi43LmfJ0CtLMNsB1SVoNRsvf73MOP2imbG56KNAbQtZGc9a7oHe5Zs5efiBuxsWgBVk62HBkA1079sm25c0JqX+Eoek4ufjmaRISjkC0/uCaBmtkOqSlBqtl7/e5gJA6jXRRsZwlev2kN2Pj0prvMNn3B8X715RZ9DVQBlr1eCmpWJ8tFR01cNMy2zn4/cAA34ck5u8efeajM+U9lUF2euu1cVplz8ElQZOrjI8MsYAL08OwLHgjwhO9UT+mr5uhDxlGuZANWOvAOAtptOuuQxufgZ0iwycktb7jvwMxtQOHPdo6owZeOX7vz0usjwy3jbmNqPq9er5HDJiN37bux/R3qg+w/Uhw1AW003SnHsgcoN0D6pSXCoyv5v5V3LP63Cu6pIa43JcQgfsLurzT/T7wO9drcEAZicAZPEBCZXPdBm1x8/4KHn3/2KWgAyASrmOvdf42RsANpmGii1kndOVM0coCKbBFBTRvAZAqgQ6hbzka0CFPb22kN2srPqJuU4IBlFv9/0Jy2Almcv8G9dP9cB6jcNlGbJYyKAin/dubd7HZ1Tk6pf8FwHVZWg1CTajFoqQ9nmoo0BtK0HilwRL4feVV/2anOayQZo9eC3b4r1drwH2nL7fKIlj8lB0F6pSRUUbavwJZ2FX2ifhvEzV4C2VAbxpMdFGwJoyByotTMeRLJrPsMAyr5+nzHWNQeKR1KWSrXkMRFAywCAzj0ak/7pzAHqrwziye0DNCBkpwr2sa57mxX41IWcsDvJv3wo3j/kEZXr32evwjtMQyVb8qh4TdgR5+LnClD9bwhqxtFgAlKTrl/QXBNADbdoP8QD0PaQnU9P2EXvMsKnUNX1vC1j39UE5V/mH1ycyt3z7GsX/MC8IGSIaeiC1rxEW/JO1X9V3Y0iW1BoeUeCiQxMTbJ+cX4cxM+cAQpkdEcfy0fdLtoUQANCdsoniuvNl+6Jd/Zu3atPEfEvi3X1578DTyLtvyVeH5wHmQYuaM1LCiWvCczrzOcsPH7N4uipSdUvDoWETQhKTRbNyJwQfayedbpoYwANPQsPdCw+3mNfuqe2wIvldbZBdP+hGLHzr8moo+8e1QC1Tf/1vzsTgC4WZkXokZpEQaHlHY3GNDA1ifrFIW22Y1BqcmhGVsN5XD/sctFkAO2nlQFQWx1i4ZnK4lrj1oreutExMDVJgAJ6g5tpyfs8AKrXEZ8Z0FX3/bENSU1azQg1o/nCuFrM1e4iAyiXl5HzBmhQZ2E2ANW9QQAVMutIAEDbRishqUmpGTnMwN64CdBeqYkRoPhxzCClD9Cg6aqFqX6piR4UpjeYmba8zwCgVh3xmAmsKkGpSagZOcwstPlg7o3UAboujs+Oiv3Xy/IR2yp/5db3T4vizxevsMNH4gqlOhgojxvy5EaxJ69gAtcsCStXmjUlXemUvG81sNFcAGp6gwDKZNcRpxnLWV6C5g7QxcLYP828kT5AX+Tr6mo5vvjFk2L/HxdX62OZTTBQBtCXj8x1d752L6zs30gdoDs7dutANBOAWt4ggJZoHSGABpkx+ckImgFAxdb3tYhpd8a3g4Ib4bVgoNX7Bw+1m+JF+FFgxcLnREGyppFRvq7HrEYRYjs9t6De6JV3j5KrLoF1RKq3u5LzS4gsgHY3YfglAoCKDfTL+kjSK+wHjsOlceK9XIpQIfzYETz4VFtJHKA7plwP9mkUybnF4Y1x+ZkcKILriFRfd6XmlyB1dR4iwy8RALRZMfrovVdvFAKg9SFNLRiopCz7tnb0vraS5iKSVay2jG+EjcrSHqoiMXLmvgrvqhy0Cu81E9C+eh/Qigeg/DhnUUiA4sFA5T2dLIaTFvyJANorNdGCQsh2AvcDART3i0MzBqgz+mFeABXIY2tFe9defnBqAhQGA9UACsKP1lbSBGit8GINahTJgkLIBQp/3nMGKBNWR1pPIgXxM1uAwncs56W/iMSRx8IunZfNHCgeDNTTA80CoOGBc/wxDgNTEzUoSsQb9ln4SVITt1+QOhJ86mJYapJpRlKIW2znZQJQxcOnJwhASxUMdKluR7bnQOcGUG+Mw8DUxA0KN0Db4zsOS03cfukM0JCqEpSaZJqR1BwBupY9zRqgWjBQuTov50a1Vfi5AXQzQ7JIAYqEvazfmDVAXVUigJ4BqUmmGUnNCaBiCF/1M02AasFAwf5QLfwoACh6CjSdkg/k56bmtCI7idR+2d4MAIrUEXNqY6LUpNOMhLDqojkvh7PwDfq4Du4LUDZDeBgMdFl8uQnipJ9EapaiEIQmVPLd+Dn1quqWAWp6w+UT6I/8AWrXEWtxbZrUJNSMuNDqAvmZwll4NQTXZQO0fPRf8dieazDXuTKDgS6LV/6fX2FP8Xig2ll4aYVHC7V+XUol342fEweH2DZADW84nKL5YwYAteqIvb1rktSk1IyY8OoC+ZnAUU4coJhWeHRQqAqg4E6kcKVV8n56qgjjM1hE4oLewM3UnhBuGZ6aBPyiA5SZWSz8BJ0bQJl7HNVFaz75ADRANUA7KqmSbzWj/wnNHaDtZgA+W7ruganZdoZCzGi5JYCa8lQGw1EE0AAlVPLtZvRJHB9BcwBFuxmTn+0EzcAvem4rMwtTE6QmjvofYsZTGVod1S01GwKoDOT523yS8srdEntHDMsv77GrkPhrK1To+bL4z0XAOjmEB7OgSxhC1FQ6Jd9uZgeKAKpahOaWgamJ3y96bi0mYGyYE0A9lcHvpO6p2RhARSDP42UhLzC23+FQFJdx7n1JANQIFXpoABSuwy9BCFFLyZR8u5kdXQTQ5pqbYIIm7xekEhBAG/kqQ7IALa6fs12ee7ebLZ3GOxyKKxYWlMUVOXaECgWLSFpEULhFVNP4MbG2KqTtBH4zV7dgAO3w9RSrC5JbiwtDf0eKfpHyVoahjjL8sjGAMhRWxOMdx6I+VATfYVCUBzTF22ioUABQ7SwSCCGqa7RiiUI7pvpGyJ0ykRuWzc8uBE2wuqC5HZmfKfpFqqUyDHSU4ZeNAZRxTZ7XBFs94TsMiuo+uaUAKBIqtAGofhoehBC1lcrYw2vGqhcSoO4Ba/JD1TAzGEC99Toxv+AFb1cE/8A06yF8mItEvTABOiw1kQFUrbGvNIDCUKENQPV4THb8EaB4Sx5X2xGKuQIUzyoBlACaP0BZZ1EHqPkOAlAkVCgEKIwImj9A5SdmuxmQmshA0f6I8yRSJ34m7xeUC3QSSamtMuiOyg2gotMJAaqHCp1vD1R9FMzP5EFhyX0Wvgs/0/eLmVs6C6+prTJojsoLoMYcKBIq1D0HOkuADkpN9KAw5Y7GRAAtKRqTUmtlgI7KC6ASihUrbYCui+ZNbBV+BgC1Z/sGpSZ6UJhyNYZuf1fS94sDoN64n5oZxyg/b4DCTDev8wIo2wf6erMPFAkVKgb5YB+ojAg6A4CKP5pzAoUpz8G8Dm7JwC9GbgPO/0Mzrp5qLgBFTyI5Mz0sNbEBVD+JhIQKFRE/sZNIuQMUQcXA1MQPCkO+9YBwt+TgFz233QDqnCvNBqDIWXh3poelJi6AquDJYstSEyr0RhMqVET8xM7CZw5QBBVDU5MAKHR5DzYHuyULv2i57QRQzWmdUpNQM/Lw08z0sNRsKSK9QzpAx1NCJe8yY7GiNWxoHqDwmUE9spHFks36BS9p8G6X1Cx0dUlNUs2IuQf/o2HFCGhpSWkBlO/trHcojaSkSh43Y1aBkP5WaqDoagbtfGYH0ICSJoC2mPFlutW/aQGULxgt8bvheivdklcyakDYjF9aoOhsBndGbgANKekOqTErEoRJtgD1Zbrdv0kB9D8Q43cVRQTG+OR3I12TVyDJSKFN9NBmCz4yCZpsyddy83NAu4oKFP0BqjsjM4AGlXRoaiyQ6DjJFaDuHAf5NymAHvyfNyt8SmRqMT6faJdwikihYM1eHuysd9drSrXkG3n4OaOz8ChADWfkBdCwkiaAes14ABri37QACofuMMbn05PiOt8NyjipIoXC6KFqWd+MqDxWkKztygvQMBPZuWWIMxqVUftlYOZsWSxxPRi3X7rJnenu/jX8EhtA60V4BkgY43OlhxnhY3wjeih7wL4yabpi2ag8/AxsV/m5ZYAzGkVdXYZmDlEgP+P2S1cF8jPEv4ZfIgdoHePz8lSuy/NOqpzw1E7Oi77nEt3+lOrYA8gLUKHOqYloqNrLDO6M4anZql8chestb1qFbzFjZjrctW2piQ2gxhC+3hwvZjvVjOha3fUBeqWcnfYIniu1kvdHkJkvQLH7bQig3TbSzwig+jH4eQP06YkOUE5OHaDsJ8elx+mWfCMfQHumJnWAljkCVFNg5ugkEirgFkemg/ybCUDh1voaoCB6aPXIwTk+gk+55Bu5Cdo3NfGAItAMehbecMYcV+HpLDwq6BZHpkP8mwVA9e1JEqDaHGj1z97XT9ERfNIl38g5AumbmohAEWYGjcZEAKVoTLg0t+CZng1Aq/+Jj3ivswl010QPFbtD0RF82iUPpMp/R+959U1NRKAIM4PODRNA+wG0e2oSb0Z2psPGcnkAtKLkwcNSbgptJj+b6KH8EetSY6HES96UMZTvnZqIQBFmBg9npzeBvAAadhKp3xxo59Tk2IwC/JsUQAu4j0kLUSeDfxbXywagMHoo/8kRgySvkjcmQ/unJiZQ9F5EWtBZ+L6r8F1Tk2UzavfvVgAaFphuZfQYfQCVZ+Hvspf1Yrs4Cy+fqx78j/6X69jvyarktQXFeUVjwrYxaY1gnNTE5Zd2fnYBqFZ7OqYmz2Y0bCYsLoD2lFp7XxW/msc2JmDGKtnFwtcGOqUmLlB0NqP/IVFuEo9kNbXR0vtk52dCU+OtPbkBFDOjfIlXnu6pmQ6g4wakc/wSLXro5ene/5QZQJG/jgRQJdwT7JEhk1p9UrI9MwHdU83MzAHauGusZpQ0QJ+eaNFDz4rDTDbSKzPI/MzC1IDUJAQKxIzDE4/bpw3z8UvIBCk046892QMUuGusZrQhgILIns2E5aGKDCKDe+pXHDWhQKvXT24Ue7/N70O6cpd/W0yI/q8yeuj/UD36nxTFv/tfsp/yiQe6s2M3EAKokhOgmNs6pSYZv7Tm1DBjuWxeANXclRpA68iecA+nBKgI7mlcstmEAl0WL/NPjpd1rGUVHPS/vsn++U8lR//b06wAurODNBACqJILoKjbOqUmFb+059QwM2+AGu5KDKB1ZE8Z74PPX0qAqthK7Jr3y3vymvcmFGj1+vo5uxd+73Z5cSo3hTbBQeGjyBB+wiBZE8socPGm1QSCzeXiFiWXJ1C3uZVwdemY08edak/CfnHIC9BgK4ZfNrAKb0b2FGvm8nhRE9yzjvjJg9jpX2CvRdRPdQMyiCICH80JoDumxNt9+ZkdQB2ecLjNqXSrS9ecMoXXnnT94pDlrjGa0eYAWkf2FJATpzAlQDkA69Pu8tr45gtLGSukvkJeDw5q2baV0NjDKmZfsfuHHtkMVR1mbE+4fNYxNRH7pbV6oBluzHhrT55D+KCW1Ds1GxvCw0OZ1cBb7ECCAK3jLfHP4RcQgMLYdvBRAmiX1EQMClw6GQigBFCvhB+yAyjvP4pB+AoMu+tYyAhARciQGqB6cNC8AMrFS97VKoL5mTIocPnOFDTPtMIkE7+EYNMy46s9eQK0+ckN0P6p2QJAGTbleF0HqLsHagEUnnnPFaDORdbQgs8FFI0cZ+E1T9AqvN+Mp/bkDVDPKvyA1GwDoNVn/684QwQBas6BegCqBwedHUA98XS6pSYVUNTCozHpniCAtphx1555AXScZrQNgFZj+FfVws8xjE2nrcJ7AKoHB50fQEUjGJ6aVEBRy3FXlPEjARSo9s9j5L2OqUmzGYEfjR7oGM1oGwCt/pERRjSAin2gH92R+0B9ANWCg+oARU+QJlry/uZBALVU9yn8VMnGL638BJ2sEVKTaDMCP2v8DPXLlgDqCUxXfSgWjDSAGieRfADVgoPCR58cFRhCUy15/e/l6KlJBhRKoYGmG4L2Sk06fgnlZ1V7CKDoWfjkAKrFuasBKiCqnYV3APT/K/79c/b+X66Dg2pwfvcoJ4CWWnmPnpp0QCEVHGjaH6w/I7+E8jNkqJo/QEuLn9EO4Z3CA4U65i4RrZv9T+FKreSVGQkC9f+xU5MIKBo+eM0sdOUQD5TnvMUMewY3YzhkcGqSakYut+j0DPJLbADFZim7ArSjUip5aMac0Rs5NdGAwmcGZr4LQAekJhK/eHuXAWZmDNDwjjkBNETplLxmxuSnXh/mAVAt8z4zC1P9UxOHX1rmN1vNdHBIQGr454MtbMiM13Wd/RI5QM+Oir3ba3PrUlk+ulEUV26LZx6xqHVXbp3LW+QO1RDeEUDUUjIlr5ux+KnVh1kAVM/8nADqKvYwM5Y3BpOiTKcZeV03slu2DlAe47P4sgXQlZgm5e/ck3OmhwZAXQFELaVS8oYZm5+wPswBoEbmZwRQZ7GHmZkzQP2uywygaxbk83JZmAB9clR89rw84zysnrlbshs72DI8WERyBhDVNF18rMmFAbS/tRTd0iHzVrsI/BWRVpfhxd7XIVKR+iVALa4b1y/bXIUXce4YNC9PTYDKcfwS7JQXBzgBQJ0BRDWNVSzbkM3PAQRN0C2dMt+zGJ1Z8QAAIABJREFUWcRZXcYo9kGciNQvAWp13ah+2TJAVViQlQFQ/bR7pY/ee/VGoQPUHUDUUiJjj+YJWfQYQJt6MTg1MQxVUTPuTHPh3w8fmMXnl5YMt/lg7qvw3dzW1S8xD+FVDDtzEUmPt3Rxo9k42gDUHb7JUrQl75AqbgJoOzxqzRig42zXIYD2SM2WASpvdkcAChaDWKd179rLD05NgLoCiFqKtuQdArE/3S0nY4AqhbJTKLhZJOCXThl3ndDSR6oDUqM+H2xhE2baXdfRLzEDNKQHyiZImzs/ZtIDVa9oFb4DR0K7FSn4pUu+nUdcoUNmA9AA13XzS9wAbZ8DVc88PfHPgWYJ0JIA2gEkgcOyFPwyCkDnGY0pwHWd/BIzQLVVeBUtFKzCw97oumhiN2Gr8FkCdGFOg46ZmhhA4TcTBFBIzOGTWo6UdDQx2MxQgCpH1A6ZPUD1qtHBL1EDlF9WfHmvEME8C37xe7MP9BHb1imG8Oxe+OYKOrAPFAkgaimRkq+lSr3+Q4k1pDkA1DhU4j2ZN15qovBLB36GReoflJpAE3E0I8x1zsFJegDV4tzJE0dfllcds6NE34EnkdjLtXz6fhPxEzuJlB9AwVQN0pBmAVD9WLN/QWC01MThl3B+ht0VNSw1YSYiaUYefo4dFXLrAC3PXlBn4cvL/7Eo9h+urbPw7GXxn8nOJ4/4iZ2Fzw6gCyi7Ic0DoFpgnZaTzWOlJhK/BPMz6LbSOQHUch3qkVFSs3GAeqXtXtK0tI5odlE6JQ/MLHShwWGHpSYSULSZaTKqmWFvGz4aKTXR+IXnHHnEGoxozyBOGSU1STUj5iK4y9ZJUAJogFIq+doM3g6UQronyYCih5k6/zkDFDdjFz0B1GPGriljpoYAKp+IruQXprQngibIEgeFzwzIv+Gj7AGKFL1mxlFx5gpQrKaMmJqoAVqHAeUAffRCsXdLrOA75j7XxfHZUbH/umU3xZL3AnRnB2lGnVMTNyh8ZrT8zwugWNETQJ1m0JoyYmpiBmgTBpRx8sv1a+fq+7p4MY9bOdsAurODNaPOqYkaFD4zRv7nBFC06AmgLjN4TRkxNREDVAsDuizYTfAi3KcRBxQCFKHnSEGyNi6rHYDPjGoRZjAPt3A5AdrDVmLVpb3ofRWngxLzi0N4TRli0fBLxADVwoAuRaj5FSOk8wTSuthDhu+pKpifgQTNxC2PkfwPQUVa1SWk6EfhZ2J+cQivKYNMGn6JGKBMdRhQyUn2gPsM/BobvjMlOPZAFlOt2mCrW2piHqqicmUbDlbzGsIHlLlVA9AR/GyG8H73cI9obSXfIbwWBlSuwl+e7r3ujsKUDUDxdhDWfDqkJiJQhJnxNgsCqKwBBFC35gRQLQyoBlBXHFDnlcdJlDwQchJJ/9jLzeDURASKMDMypy6AjpWaiP2CFz2dRELlAOioqYkXoHoY0MAeaFYAdR/gDeNnyqDApbLq4WfmAMWLns7Co+LhZ1r4mS9A9TCgkpMsgr1vDjQvgDpDyBBAEYCOl5qY/RIE0JlHY1JCATpyauIHqAgDKi/dhDd2ilV4GEQ0O4C64lsSQN3NggDKZVUcAijeUrIFqB4GVF77XjT7QGUcUBhENEOANtJaRBA/kwYFKjC14cx/5gBFi953pceIqUmxGaEtpZtfkgJoHeruFT0MqDqJdJ09Bk8iwSCiGQPUHJOF8DNtUGDSF5nx/OcOUKzoW++tGCc1STYj211d/ZIqQFVEUDnV+crZUbEn4oPCs/Dl5Z06iGi+ALVXBQL4mTgoELHcAleg+c8eoEjRt96cNk5q0mxGTn6G+iUigFbdxWPPxyvPp3x0f/nm9V6/N7WSN80sFkapM7XQMyA1kYMCNaO5ot+cVvp+MYseM2PWmRkCVJnR3NXdLxEB9MmRa6s7VytAnV3MFqVa8lKsAtgEHZ6a6EGhy3AD/GMybmpi8Yto+P3MWI6aMUChTL+0d0NiAujy+f8GOa0epPkCtB6CzBuglhtyB2jIJI3bDAE0BKBBPo4HoE9PDs56InC+AAWT4Do25gVQxA04QXMBaNAyodPMwhQBlMvBz/5LCRsF6Lo4bjYqsbuM9x+uxFXFIhCyHMI318mBXfLVF797KiKC4jGUL+4cFcW1h+gvTrjkd3ZsdIyVmihAEWYGc0PWANUy3NmMxU8CqJCLn703s2wUoMtq/K7u5hDbj/a+JAD6IrisWF5ozF6GAFTGUH4CtjZZSrfkd3TNFaCoG3IGqJHhrmYIoA4zTn723U69SYA+OapApyImrVi4ZBZv6bgJhMwBWpHws2zLPOup6gBVQ3gshvLTk+I633ZvXao0UmTBLQklRz9TKbvFCdDhpuOsLkaGO3/fAmhnC3H6Zag8AA20YPhlkwDlfJQnNOWB9oqWx00gZP6APKi5BCfgvQBVXz1sfoemSctjau2YGgCNhN2CumEcfsYJCivDnS0M5WecfhkuJz/7XeywSYBenvLeocAf742qn5pe6XFZhwqpP20B6IEI2SRX95VdXUmNPayCNcmhyntwaiIYqgaYcbkBH8EnOIT3FrhbwIzjLDwcwc9xCO86Ee0AqO3XgNRsEKBPjsSNcHyUvYY9RgjQOlgdUweA1keYsIuRkyp5AqguAigBtJ+ZzAC6gsc0DYA2P2DXInkByr8KzoAmD9ASlDxGjvFSkwZAy5YIyuOnZvt+aW/VxuP2ewY/CaBSboD2Ss3mAAoYd6h3Og2AduyBSoB6N+inW/Jufs4JoAG3046Zmgj80omfdBYeV0uMlVAfRwLQehP8k6MKdsYcaAPQznOg/Kvat2ylW/IWOcZMTQSgCDSDAnSy1ETglxEAStGYHH5LFKBqA6iIKCKJV3VLDYCq9XQQOplHSwYAxWIor2SPFr9XLt2SN8kxamoiAEWgGU9H3ETpzAG6cPlm7gCFvhCv0wIomNuUl7tX/dB6HygAqNgH+uhIzJTW0ZIlQBkesRjKFYkPHpbl2RHaE0245O2e13ipiQAUoWZcVV3vZY2Umhj80oWfTX2x3TFOakJNRNuMUMcE+jgOgKqNmiVH5CvGSSS4iXNVT5TCaMkcoE/4qSM0hrIMslyg8e4SLnnkEPhoqYkBFKFmWvi54Z7WBvzSgZ/2HVoIQWcNUIdjwnwcBUDhJKUYfIuz8NYcaKWzX1Fn4cs//ZyKlix6sO/yeHh1DOWnJz9fHIogocviL7Oz8HfR355ayWtmTH7O8ShniQcnWizslpELQHtEY8LcMVZqwkxE2oycjgnycRQAdWmJDLnBaF/b1GTLXlnClWzJC2n05HWAvTMzgCIRpBcLpGU8bg81nYpfRD6CzaDucKQGdVGuAHU7JugvVJQAZfcTl47tRwRQy4xeA0L7JqmAoq8ZHKDt3snUL+EAdbhobgANbEdRAvTpCb9zc4ktmhNATTMOfs7xSg+ghSn+boB38vQL7g7MjMtFmQLU5ZjQdhQlQNkNxXwVCdn/jgBUC/b56Gb1xSu3zksjxt2TG0XxEnpjSKIlX8vFzyElH5SQGEFRC20YId7J0y/BAHW6KE+AWn6RjgluR3EClGNw7yUsALINUC3Y573mPJMG0Bf5Q+idS2mWfCMnP/OY6+tpBmsYQd7J0y+hAHW7aFYADW9HkQLULQugWrDPNQskynqwVedVG8KzjaDIPtBpo2NtRh6AhhvJzi1Ww3jcJ45mNtUFcwemQBdl4xeHY/q2I8MvcQK0aMSYqQX7lJOdfF+UBlDe97SnQqcqlo3Kyc/eJT9hYjemVn6GeCef6tKLn04X5eMX1DG925HhlxQAagf7/Oi9V28UJkCbE6CWUht7WCMKD0C5+qUmuaFq2zamQMdk6hfLHYYZvOoAR+U2hEfOF7ibkLu+JD+EN4J9svOfMiqeI8KIpVRLvhYBlIsAiosAiooAKl7qwT7ZgtLetZcfWEP4nAHaI4phQGoSBUUjCxizBmjYSSSPi3IFKOaYDu0oA4CCzU7sHKiMQT8ngHaPYhiQmlRB0aitXfRLTbJ+8fBzjqvw3rPwI/VDUgCoFuxT0bTqls4KoJ2jGAakJllQNDLbxbwBGhSNaY4ARRwzI4BqwT4VQNeFuv8jS4BiTUCUPwFUV49+Rc5+Ec5A50Drh2cIUDse6JwACoN9iiH85f0CAJThNSOAssJ1dCOYgvmZNShq+cbwPVOTul/Qfuhj8LTDRVkDtBE2lOudmiQAqgX7XIuXB/f1IKH5AHRRA7SdoMNSkzoomHyzoH1Tk7hf8JnQx/Bx3EXzACi2mtA/NXECtF51/w9/iQN0BYN9PrpRFP9ecSzG82yK9H/+j3MC6GKBNwCgQH7mBwrbjO2t+UZjknJUIPiIw0W5ARQ1A5wzRj8kRoBqO+klQI3zRfDA0RplpqaESn6xcDQALlXa7N/hqUkJFDznlhnEW4/ziQfay4zOB9dVJ6iLEgAoSHc/M3qFCeiFpAlQNYZ/dITTcZ4A1f9izgmgrr4CDtChqUnHL4gZV/dqI36ZuBlpeRoDoIvBmYocoCrwsqlsAbowBT4z5mxmBFDXbBXmrZkD1OKnclkGANXz1MuMXWMyB6jax8R4KW5RUhFFZPTPSxnOrrxgUUKv3kYNbrvkw814ALqzM0IFCk5IoImNmDFzXosAqptpHGG7LH2AGnkigDoFALouaoDCezyb6J8KoDJkKN4bzQGgOztjVKDghASa2IQZK+e1CKC6GZyfoQOWyAFq5okA6lQN0MszMQfKAbpigUDlTfIw+icfwlcc/ew5mzO1QtxPFiRrElkFXH9i1J/uppN1izvnbm+FK+nqYgsFaB9D0fllhDyNUWMMv0QK0GYVnp9BYgCVBzqrnuaxHv1zLWLTY5eDMPVz9NYUyM8eFShVt/hyPpyf8YFioBB+9qJNbH4ZI0+PR6gxhl8iB+iV2/wMJwOoCga6FABton+qHuj+A6fBdIbwpexAOMZiqHqnJoGhakuWzfHYOKlJwC+6mEOUa9pqzYDUbLYZhVR9f768B9dUjcl3CH95v75yjgF0jYSlbwBavay0/xp6pxwBtEdCwkxMbaYlywRQLgIoARRKzYGuChmGKQCg5dkL4rRn6pfKaQBt3rYrzFwWkbxNxeLnbAFaCwPoOKnZajOy8tRuBqWqWWNyBmjFSUXSYxkzxA3Qqsv67Zs8xIitlADqOgtvNYm5ANQfYcnkJwEUIehIqUltFb7lLHzoiCVhgD49UXOd1hwoAtCSD/uxe42TAqgjGhMBFAWoFXqIAFoSQNU30Le7jlgSBqgaxINV+IqpGEDViaX63jlNaQHUigfKC5sAqgEUIFP/WzNvgEpfEEDFN8APsJbA13kDtKImA6LaB/p6sw8UAvSAH0hiIUMvTpOPxsSE7xY3msRsAIqcRDK7neOmJhW/1LKu9EBcljxAu59E8kakH5oa9Ttav74FaWfhBTutk0gNQEU8UHUSaR87Op8yQMGUTdcuRTagcPKzV4PIxy9KiFusLnv6AO18Ft57J9Lg1Mjf0fr1SaRwJ24nNgUAKtaR4Fl4aw60fPeI9UEv7lQ2r9xC9zFtu+QHmNEWDTt2KfIBhZOffRpERn7RzOhuMfiZA0B7R2PyVZgcAerVsusXyghKvreZhS7QIEwz8KPA1KQECjHtZYMCbRCIL7qkJiW/NGYst+heaPFJSGoiaEad/iIoM94Ko8y4/RMnQLG1npav8G6p+8ymRxGUfE8znrLXzJj9jaDUpAmKVoCivuiSmjT94nVLq09CUpNqMwoAqM8/mQCUbWk6Ly+X6D6lFqVa8t7woNCMNeMVlJosQGE1CNwXXVKTpF+8bmn3SUhqEm1G/grDn/H6JwmAsouOrshgnhd3qoH9Sw/Zy2q8zk4Y8R/OxIj/3/nv5AdPbhR7v81nQK/cLcE+e2QZPtGSDwbozg5aA7IERRtAHb7okpoE/WI5xRWK20fQ2QLU758UALoScOQrQ/IKzj021bksrjY3I/3fvwQ/eJk/drzkb72uFp7kblFDiZZ8KEB3dvAakCEoyrb24PJFl9Qk6BcvQEN8EpKaRJtRK0Bb/JMAQJ8csWieZ5yT1euXPmAr7myus8JjNW4XcT/ND66fs9vh927LzZ9Luc/evAKkX9SrSGSVPfqUUQEctnJxi98nYb4AKjPxi8ctnX3ClItfAhpRJ/8Yftn+KrzYKM/H3ZyBagy+FFRs4n4iH4hdomL4Lk502hckTVUsm1EPfrpqQDZu6cDPAFpkU11C+RlI0Gz80tqIuvnH8EsUAIXj7vo1pyKI+2l+wMgpl+T5O6LviW9ySm3s4Y3XppmxCl8qKDXJDVVFvhw+cbliiut7Y/SL5RanQ1yOyW0Ibx3QMhpRoHPiH8LDrUn1aw5EsGEe+wAClLPTHsFzpVryBFBdBFBcBFBUMwJogz0QickEqP6BmPEEAGWjd8cVx6mWfOk7RPG4edpR9nmCwnuwpJ2eAalJ1C8utwT5JCQ1yTYjT4WB+4Yd/kkBoD16oBZAn54cnDuOKSVb8qXnGO8MV+GRLoX9SDsrcvWLyy2B/MwXoC1n4Vv8Ez9AfXOgNUDNDyyAlsu9r5+iI/iES750B5KZMUA9wXXmDFCXWwig/mhMyQNULbDzf4zF9mPXBzZA18WLR+gIPuWSZ0ILftYAdflk5gB1uIUAWrorTBYAFftAH4Htnh/dkds9tbB18AMboOwyT+xCj8RLPsCMo/wzBoXvmVZWzM4vYfzMG6A+M37/xAnQwtgIysX7j/pJJBC2Tv/ABmg1yHcEGsm25JsvYOU/O1BwM+2smJ9fgvg5X4CmeBZeB6j7LDwAqHFIvjQBWj3WI9BIHgppH3MR+cIW+cSv3v7JyqErxwieRCKRplBOAL087REqlEQikfoqJ4Ce4bvoSSQSaRrlA9Bl4VpCIpFIpEmUD0Dvs8h2JBKJtDnlA1ASiUTasAigJBKJ1FMEUBKJROopAiiJRCL1FAGURCKReooASiKRSD1FACWRSKSeIoCSSCRSTxFASSQSqacIoCQSaSw9ulkUeyLi5EA9PRkW2eLyzpGKfTllSgigJBJpJN0TIX7HCEqxHBYaiF1PUQm/I23ElBBASSTSOFrzeBQXp8O5dbkshgF0WRw8ZCkZHGK9JSUEUBKJNIrUxblV7w+9XTxc7IKKQQB9csQZDi9MnyYlBFASiTSKnp7Inudy4NUQq6K4Piy6r7rCd+glFa0pIYCSSKRxNRig+3fL9SCALmUfeJiVgJQQQEkk0qgaPnAuB6Kvvt3HuD99/JQQQEkk0qhajXG3DgGURCLNUOtRtjGNBdDhG5kIoCQSaVNaH+0NXIMXZqgHSiKRZqC12D4vsLnq3f/UzBBASSTSLATJd6//+H1EgI62Ct9qgwBKIpFG0uWy2B/h8CTTMPSp/Z9D94G2poQASiKRRtJy+NFJpWEAHe0kUltKCKAkEmkcrcbj50CAXp4W++OchSeAkkikTUhGQCoGnmMXGjh7eTFeNCYC6Gb1g2d/4g/K8uPf+4zx/o8/z97PTj/+/O5z9Usrh8INb9SPuGwEuObD3U/3SuBGBHJgZ+Z7LPt2hVACGfvBs596R7z6xu7uF8UXWxSRW9ZFPAAtL+5UyXhpjA4xAXSzEgC163W+AH3md9VLK4fCDQRQX/IxgH6P8TM1gM5RBNDRNT+AqqwSQI2XUJ0AKvgZpKjdkr8IoKNrdgB95idlayeAGi+hugC0Az/jdkv+IoCOLgbQj7+6uys6Zn/869WLT/zNd0TD+nBX9TCyqfVVtv6PZwUyFDp++LVnd3d/+ltlqdxQAfQHX9jd/VkxPK0/Zq3/ubef3f3kPxBfbHxVfeOLb/9k9Q3+VFk988xvClL8kD3zU7+5hYx6ZQEUZqAaiTcVAuReyxiXAOjbip98CK+54uNv7O5+8ltyaA+//cOv7apnqi9U3n7mb5XlHz27+4nf2YwD5ioC6OjSAPqNXaFPi4Yl21n1cXAPI3axLMm/BzJ3P3iWZ/mZLwKA/gx/j//1aD5mAGUffOr/4l8Evqq+8VP8NTf4Bn/5X7AP5Jej+/uDALTJAAQozD3MmBAH6Id1/1MCtLEkzDzzNzhA4bc/BGbf2P0N/tNz/AE1QU2aRATQ0QWH8B/uPsN6AG+zaswb1hu8beQ0mmd5kX8QRLZ+/Pndz7xTfvzN3cYNVUv+1LdYh+mL1sccqvyL0FfsG59+R36j+uA3y4/ZOwwgf6Z6/o+fjQ0LCECbDIBFJCP3dcakGEArn6gOtgQotFT56IdVZ/45/dsVln/2X7LuqfTdZ96pfgX7/Idfje5vTV4igI4uCFA5+ccBIykhuJpPrebZEnkW6FDTEwAbb4i+J/eG8TEnofzb0vhK+4b44GOGgh9/PjZySiEAhVmuPaHlHmRMqgIoG5erPMqnakvyD1WFS+Pbyuwb7F/xBfFQqSaNSNOIADq6zEWkH/2Lv/+FXQXQeoJsq0kcUypHcpKiatGy/cvhqAAod8b3eB9S/5g37xo/ylfwGwqa4tu7n/z9jWcxQAhA6wwAgGq5hxmTYgP8Z36yZp4EaG1JLdIzdhpuERWK+1OQVX5OAJ1WBNDRpQGUDbe4vgjYmdMIvh63q0kKMU2nZu20jrhs69rHAKDAV/AbylsfChZV+uR/Hx0TEIDWGdAACnKvZUzoB6z7Wf1PvgH6qdzSh6D/Cr9dd8xBV54AuhERQEcXBCjvUfz0b/x+PYTn7+c0glcNmTVU2cf2AhT7WE0DNL6C3/jBs5AzbEF6l03ybSm7DgUCVMu9njGuH8iZTn0VHgUo/Hb927V5dgLoBkQAHV0AoGyCilXfZg60+v+n3slpBF833qqdS4DCaUoEoPbHaiGq8ZWzB8oe+N/YTqb2IzobFciWKH8XQEHu0R6onO9Uw3MToGoXnL8HSgDdmAigowsAVFXsquNRD9zfeOYffjWjEXzdkKu8/gM5hAd/HrAhvPWx3OEFfIUhF0wVfvzN2LAAsiWQ5RzCN7lH50DllMau3FD/nNEZD5sDJYBuTATQ0YUA9MPdpsvAtj5mNIJvho8f7n7iWbEKL5osb7oWQLGPNYByX2nfAMvNatzaBN2IRSpbLJ1ikyYGUD336Cr8O+Jf+fRz2F8fEb/FswpPAN2YCKCjSwG0qrhiWMq25DUAZdNgkY0/B6mZ/XtDbPZm3advlfUeTrM7ZnxsDOGlr7Rv8A057AgOJwj7coS7G6tE8v0Bf/IFtfnfAqjY8QpyDzJWW7GDieiWKirKfaDw22If6I++JveBEkA3JgLo6BIAZUsifFM006e+2czlVQyIdC9jPzUArbIs+tjytNBnSuUGyADtY7iIBH2lUUMsvesnkT4Z3SSIypYsXQugwhNa7mHGhGqAVrWkcokFUO0kEvy2fhKJALoxEUBHlxxm/tGzrOr+cdVb+MRvNmvUJRjs5SGw/vy9XXgWXhzC5m7QcAg/hgCFvtIBypbe67Pw7MvivHxk+vj32AYBlTQLoLJCaM6BGeNqpib4NKgFUHkWXr4Fv62fhS8JoBsSAXTjCgnySCJ5lNU2jrRFAN206sMoJFJHycFNtAdaZygC6Kb1dnTrH6RUVI3r2ULbGzQsj0YE0M3qDYovRuqvt+E6FSkCEUA3q2+yGGMkUk+xmNPPyDDTpAhEACWRSKSeIoCSSCRSTxFASSQSqacIoCQSidRTBFASiUTqKQIoiUQi9RQBlEQikXqKAEoikUg9NQ+APm5/ov2RDZrZCSmW4akJSGxIhjZnJsQvI6SG/EIKFQFUPkEA7WVis2YIoLgIoFsTAVQ+QQDtZWKzZgiguAigW1N0AH3/zlFR7L300Hz/6cnzb5WXb17vZZQA2tPCPEFBfiGFKjKAXp4WUsfGJxyg6+Kwl9nUALohM8mBYkNm4klJfGZIuuIC6NOT4srt87L86J5FUALo+GbiaprxmIknJfGZIemKC6DL4uBcvFoVFS+hCKDjm4mracZjJp6UxGeGpCsqgD45qqlZjeWPGVB5P3RVcbMC6Hf5+P4Qvlsh9fjsqNh/vSwv2OTpNWvulCsaZMVlJq6mGY+ZeFISnxmSrqgAugTj9o/OyzCAvlhxs+q3PjniU6d7r2CGo0FWXGbiaprxmIknJfGZIemKCaBVr9PAnw5QNYTXASpG/U9Piuvn5eV9c+TPagWJi9yCiqoLrvHaddaKCaBPT/Ze198JAaj4zkrOjq6s5fttV8RoRG5BRdUF1zhtOnulD1DeAb08lV99cqSWoaCiGTTHZSauwWE8ZuJJSXxmSLpiAmhNwVodAKr2j9pj+DIiZAWaoY30uGgjPS7aSL81xQXQPnOgfOj+9IQA2j01BIrJTGzWDAF0a4oJoPVEJtOaLQp1AKjZedVEAO1pYZ6gIL+QQhUVQME+0IqShx16oHbnVRMBtKeFeYKC/EIKVVQABSeRzvjquuiSVnTUAArerc8mreQ310UOi0hVg1hUmjo1CYFCuIMAigv1i1GDCKCTKC6AqrPwF3fEWfh1UdwuL06LBqAMj+DdGqDVNw8eVtw9Qnui6QF0ITVpapIBhfIGARQX4peFWYMIoJMoLoCWFze0aExicf357yiAPuGnjsC7zen4tTiJVKDx7tIFqIeg8wHoggDql+2XxcKsQQTQSTQKQC/ffKFC17W78geLYb4wnhc3i2IfnGB/dBPGA728wz5dF3+x+EUG0PLdI9YHVe8eropfrdedxFn4u+gvSQygCwBQN0FnA9AFAGjLtMY4qUnEL40sgC4WFkEJoJNoDIDKY+gV1NgSEBIxyRNESXzXu4TusWCfO8KVFkAXC6T+19rZkc1lLgC13FF7YKrUpOEXjxnDZdxhBNBJNAJAqxE1m34sH93glOsG0GVxeC6Wg7zqG8dOKh+A7ijNFqDAA1OlJg2/eMx0cli31JB0jQDQeuFbbMbsBFDxFfz8ZZiFICUF0IUp8NlOo7kA1MUl63CbAAAgAElEQVRPFxAIoA5+thOUANpdIwC02f6+LI4vZcg5NplZvbhy67xs3rJDdorTmyYe668ynR0Ve7fXYlfoK09uFHu/zadCr9wt5RC+evvshaKwr1FqlBBALXxCgu5AzRKgmgemSk0SfnGYMetOu8O6pYaka5QeKDg9WdPynpwXPWzewkJ2sv2bFzf0vUfNV0uGR6YvC4C+zC0cL9W8qQToVecZTqlMALqz06U5ZAIKNw5wDxBAOzqsW2pIukYA6NOTYu/WB/WPazkTune3VPvhxVtoyE628/NIXwoyvrp3u7xcFgKglYHq++ytCz5vKgFaHJ67doCyWpGYLH7WnxitoaPdRN3i48EY9lOvLohGcVhPGsxNY6zCi82b114TENWOW4ojlvAAkbl0vi4ELxtpXxU/iGUmcU7pyZHcY1/9IAF6AKLXW5qsmk6mQH525UeqbnHjYBSCJl9dEI3hsO4cmKXG2Qf6SOx/f4mBrJnQ/Oi9V6v3FUDRkJ1sRye2iUl9VQUJWTUn4+VbEKCH9SMOJTSEL9tGYS71SE0CQ9UduCXW74rxUpOAX/xm2h3WOzUkXaOdRHqfbaZng3MJ0PpMUQNQO+Ic2wH1YMl+fvcrzRoQ+Co/f1RKC06ANrFFHEoNoOHUIIASQO2N9ATQjWnMo5wXJwxlAqBswWjv2ssPmiE8FrJzJcl68H0QTQl+VcVnUqvw5RwA6jqJFNYKOqQmEVB4ADpNahLxSyPnSaRuVYYA2l3DAQpCcXKoqe7moYwU3wDUGqrLUX1FzKtNECXtq0YPdCYAdZ2F78jPbEDhBOhEqUnFL7XcZ+E7VRkCaHcNBygIxdkAVNGy6naCIby5TK6eWouRvv4m/6oxBzo/gBpvzxOgzg7VRKlJxi9K7mhMBNCJNcpGejUkX9bxkQAaj5tVeDNkZ8VUteDe9E71r+qr8PMBaInwc74AbeKBIh4w/DQjgNY5d8cDJYBOrJH2gcognmLn5sG5HIezPZsCoAyZSMhOHtlTLBrVBNW/yjctXd4rZgpQ3xh+eGpSAUUtBAeWn2YDUJBzDxm7/MklgHbXePtA1RkjEbNzLd45uK+OILHBvR2yUx462rt1rzmgpH214iI4idQK0CdHaGCn9AAKlp71D5rGMEuAli5+jhn3Mgm/wJz70NhhyEIA7a6R9oHeLDRVbGNbQ6/clp1PEcZTC9kpt9Ozx3j0z3vNNnjtq+XZC+AsfMkA+vOfe/6t8l8Vf24mAMUJOkZqkgAFFMu2i58jxr1MwS9azr1sHG/Kh2RrtG1MdVBQ76H0RqGhPBHxpflO308NoIsFQgapuik8xt7slJroQWFmS5hp3sX8NA+AGjn3m2kGLH6OEkC7a0SABnFzDKm9TeHKCaCIGVcfI3FQ2NlynrghgIaYaeuLEkC7iwAqn4gJoAtTfjPOWa60QYFkyzCD+mkWAA2rIYaZ1tlQAmh3TQZQdrGmjNG5Lo7Pjor917WAoGIIztbX9x86wnrK+J8yLmhjEA7h5XxpszkKXYxPCqBW43C0D2XGvc6aNCiwbBFAS7x+BDD0sbuedEkNSddUAF2D2J/r4kWxDA8DgnIAivPxe1/Cw3qq+J+HhkEAULFGz17KbimyXb/MG6A7O86WkTIo0GwRQMveAPXUky6pIemaCKAVKl/6gPUvxc5Q0TvUAoJyAK5YKDu2CwoN61m9c/BQvgMNNgCt3v1s9R1uUK3Rm8P76YKGTSarbfgeNhqG+8G03BKSrU5+cqlMyy9CPXIeXE+kxgJD5ppgFR7G/pRnk8B5zLJm57HqLooIn3ZYz6Xoja7Ejs/GYAPQlYpaXwcdtS9PGqG6blz9+elpGUm5JSxbI/AzTYB2z3l4PZEaAwoz0DQArQfS8nT8gYgOAgOCMgCqyKBLPKynZCnjpWawBqg2Xhd9zyUalz6pIXzZugpvNQZcSQ1VA/OksiaE+Sn7ITyXCVDHd8NdGZoakq5phvB15CVOtRqgcKcoA+AadkntE5kShgy8msEaoHqApyWI3mQoNYDqG+kJoARQU3rGnRuTCKBTayqAyh8kQA/F6yEABQYBQCEumTXH9cdpA9RrZkiDiBgUeLaccS+Bn+YB0A4nkYR89aRLaki6NtMDPdTe5BIAVVuPxuiBPj05OMdH8GkDtMWMp12kDAo0W+64lws6C9+idn4SQHtoGoCac6CH2ptcyByoDdBjZTlgDrR6fu/rp/gW+/QAikdjwszMG6AUjaklGlMtAugkmmgbk7EKr0bqICAoAODTExdA+fMrcYGxZxVe/sP2m+Ih7RIEqBXnUhNYOJgnQIFzDD/NBqB1zoOG8ATQaTTpPtCP7oBr4Y2AoGof6OvNPlAboM3OUGjQ3Af6SO4dZZOseISRJAHqlt7rcreLpEGBZcsKIjJNaqL2iykxB+obqkgzrfwkgPbQZk4iHWpv8oCgyEkkG6BfVueMWk4iia9oge015QVQc97P2S7SBgWSLRCD3knQmQF0UQO0haB0Fn4SbeYs/GH9Zh0QFJ6FR+ZAVzC86LFu0DwLX/wlaX7VXE2nKyuA2ivPrnaROCjsbDmDgI6bmsj9ArUAAPUT9DFFY5pCY15r3FvI2rkNUKcaPjtjhKYG0MeP3aEbFwsEIPjDCYJCzwg6qYs6YNTUROgXh0xf+OjI/uf5PDA1JF1bBajstSIXHnMti18JiVtXA7Q+6mQpPYC6Owst/OiSmuhAEdRFIoA20l3hXyEaJzUkXVsF6NMTfn/c0jHy7grQM+e1cskB1D1dtTA1IDWxgSJokq7NAXMCqIOf+C75cVJD0rXdIfyZvFMO7zgCgDYhRfn1S1dkjNDqPXlfUln+IbN07SFqKTWAuhZMLXj4GZoYKFy51s0QQB31oMV9BNBJtOU5UIbDvZdw6ukAlSFF74GQT2yfk7qxs3zyy82yv6XEALqz42gMWQPUmWvdDAEUrwdt7iOATqIoFpFcas66q5CiaxZAlPVcxf7SvdtsAoABVIs1qmmM4GEbltEWtM+shhNsNXa3+HIN1N8BuFKuLh6ADrY9SYvOT8kAVAzz5V4ncYRJ/FC9BieV7IX4EerphrVjSvu0Nz4id4s/10Dj8jNpgAJfBLsvVGM35kyVDECbdaaP3nv1RsFD1zVxmvVYo5YiH8Jbld+nwOF7SGoiGKqG5Ngw0+KAOQzha7d5AGoBdZzUkHSlBlB27pOrif255gD13kpPAA1LSHcTQ810A2gdI4AAqlWG6gUBdDtKBqBihM7i3u9de/kBG8Krs09rHl8kZYAa8hCEKZifaYGiJdfyGfZ/vwNmAdBaboBOlBqSrrQAyuY7ZXB7vQfq2osvlRZA29ajQ/mZGCja+Uln4W0pgNIq/HaUFkAVJ6sOpzkHikdSlsoLoAGxQsNSExcowgFK0ZiACKBbVZoAXYMAeGoVHsQatZQWQFvbghYPdEBq4gJFB4CCIKAWRucGUBUP1Ok+6SEC6CRKC6BiCH95XwQY4dchs3hOh0asUUspAdRaUp0uNZGBoj3X7oOto6YmMr+0P8L9gruv9hABdBKlBVDW9WQ6uC9vn29OIsFYo5YSAqi1J2XC1MQGitZce9bTxkxNbH5plfCLl5+t8z2BqSHp6g3QyzdfKFRsT03OmHKBurjJQoTylxKgl29erwOGsPifV26rkfrZC8Xe7X9a/AL/XhNr1FI6AF2YAJ0yNdGBoi3Xxgfocvx8AYq4L3zHRmhqSLr6AvSJ7PAV++auoYEAFYa1NXXHXcVKy/ZfGCNAUU6A6u6n5yipiRAULX8zNDMLXfK7cwTo49p3uvtMD42QGpKungC9POVTjqxD6IVbdy2Lw3OxLlQrR4A6+lpdanyWAO1iRvdVQJ89NDVJ+gXLPwF0cvUEaL3Y3bIBs7OEPf1AZoYAdYzRF6amTU2SoKjl4GcAQXP0S8sEaBBBCaDd1ROgzdVvy+Zuoiu3xUfyCqMjFZ5zWbxy9oK6IAk8qT0kJQ61Q2SKU5qH5tONmSoFT6ofXsKvQ+KKDaDmgqlV00MqPgEU52c7QTP0i5b/turkrFEE0M7q3QPVT0yumsuLBECfgFs5l8XV5pAleFJ7qLF0cH5xA2xGqgGqPQ3MLHms0MJ1oRxTZADd2THaOwG0hxknP1sJmp9fjPwTQDemngB9elLs3fqg/lHcz37GEckBqoXnNO53r59EY3hWb1450gfloj+qPQ3NVOYPHrp2gLJaEZ2M6t58YNXzMX9r9G7pKA9Au5iJv7oECM//oOrUDwyzU99VeBEV6dprAqIrFSH+FQlQLTynvPNoCT6QT2IxPNeFCJoM3zksjaehGWge0QTVdaB2TDUfTcjP7AA6UjDM6KtLgFz5H1KdutBgxuq/D/SRCCzHph61k+gMcHp4zqW66l0/s47G8Ly8Y9+RxAGqPa39QmDepW0O4a3K7VPIQGtQajpaiG2oCp4JDYbZIzUx+2Wy6hSYGpKuQSeR3meb6atRtLYULwAKo8vJvuHKiJqExfBk26MeLNnP736lXluSAAVPa78QmHcpNYCqF+OnpqOFyACqxwYlgBJAt66hRzkvTip8NUcuSwFQPTynBtDmSSyG50qy8uD7oI+5lofdIUDBL4wcoLZ8bVwD6NSpiRkUqFwnkUKo2SE1CfrFkf9O/CSA9lA/gIIeINsRavVA9d2hjh4osoVUjtOfHBVXwaK6BCh4OqUeKPKQr7UDgE6emgRBof/sIujA1CToF1f+u/CTANpD/QAK5iAZQJE5ULgkDginz4Fa6+YgXB34TA3hta1NcA40J4DW8R3bB1yDU5MgKIw3CKBcrUOaBUVjmki9N9KrMfQS3Im5Eq+PjfCckHD6k2YMzwqM6tZN0MVUq/DgaWgmM4Cq+I7aG/1Cr2cIivql8on4d6YAVU7wAJTigU6rAftAb5+zw0GFOHrJtmU+Oqq3MWnhOSHh4JNIDM+q63lb7pFqCCoAqz0NzaQHUEfoxkZ4H2L01CQDCqXaL5ZPuvAzF780Tgj5+0EAnUTD9oGaB4NET5HxDIbn1AgHnsRieN6TVm/daw4osSNIFUK1p4EZnc/o0fzYANoW+TJoFmvGAEV80oGfmfgFOKHKc2v+CaCTaMA+0JsVwK6o40iOs/B32Uu9iwjPwv/bz0kMX62PNbGP99ix+XvVl9494qc1q38Ozo2In9pZ+Np8KgBtiXwZFNFxvgBFfRLOzzz8stAB2pp/Augk2m5EerA3ae+2/fG6PjU/UPEB1B/5sjHDl0dwgs4QoMLMYoES1IgH6mNJBn5BnIDluHmPADqJtg1QuRb1/p0COcq+EpcYD1eMAA0yU3crCKCNGRdAoRlvfywHv/idIAW9QACdRJEAFK7rNxp6O0itVAEKJrbshjJXgC5MIWb8M4IZ+KXFCUKaFwigkygagKqtnU3Qz3W9TOSKLcquTyquOmOLNkoUoNrSqtVQCKBOgGqO656amP1i5d7tCt0LBNBJFA1A5RoTCPpZA9QVW1Tdy8TXprDYorXSBOiOLgJoKEANx3VOTcx+CQeo4QUC6CSKB6A8JpMeIlQM4V2xRas+62fPy/IRX3hHY4tyjR88bFPCADrAXC5usahhPWGiw6s0q0u7E7p5wdYU7T1DxQTQ6rUeIlT9H48tapzAx2KLMo1RXbeiHVPD+JkNQFujXFqO81pLtLp05Wdngo7QvOeguABqhAjlNPTFFt1/IL+MxhZtlMgQ3qrzFkDr16OkJuahqseMdwSPu65balLwi+2Eltrjd0lwaki6YgOoFuFOAtQRGk+cRtp/jQETiy0KRADtlpBwE5s1I5o9AbS0rtQrCaDbUTwAFXHxbIA6Y4uWJVuPZ2c7z9HYokCJALQW3iDoJJLvJFJjppUVWfjFAqihEGL2SA1JVzwANcOFljVAHbFFmS6/fZMfVmq5nj5JgNqr8GOnJglQQHnOwkMzbeTIwy8tAKVV+I0oGoBWfchXzBChagiPxxaVurxvRiS1lQdAR09NGqAAckdj0szMA6BaNCbkYwLoJhQNQO8VYtkIhghVq/B4bFG+bF/KdSM7tihU2gAVfQx7IDo4NYmAohESD1R/Wc4JoDAeqPUWAXQzigOgl+/fFPE/9RCh9T5QNLYouzqpev/iVF6ZZMYWBUoToGWNT8dk39DUJAMKJaynZfrncZn1SSRUeM9c9wIBdBJtG6BNNCYBPiPo57H5nhGcmWv/LfOLphIFaLmjXaw4emrSBUUjyz+PxYMefubrF8MZmhcIoJOoB0AVt5h8E48BqgF69ZYaeMOgn6vi13gXVYst+lcYIv958Qvq2Svyq1q0UEOpArTUL6YdOzXJgqKR7Z/H8kk3P7P1i+UM6AUC6CTaLkBbpd1fzLUGYe+DFQFAQWPuYGaxsAkxRmpCLUQGCssM4h/1yJx6oI/F3i2ksjQeIIBOol4AxdZpppELoB21dYBqHSIC6GhmPAD19ELz80ud1SEzPgTQHiKAyicmBag+JRduZmFqlNR0sBAbKIxnMP/Uj7jnQbPzC8iqk6AE0Ek0BkDhLUcXdwoVrVML3Qm0Lo7Pjor91/UQnpf3imL/oQwYUi8USYDK+5fO5ZHNw+bipdbfxrVlgBqLwoFmLDoYzYIA6gWoZyU+M78stKy6KgwBdBKNAFB5QSZcMLdCd0KtixfFNZswhKcg496XUIDKqzrF1iUA0JDfxrVdgO7oIoCOZsYDUNPpXVKTmF+MrBJAN6nhABVXtJ+pEMcvfcA6k2xTJwjdqWldiM3uWgjPVbF3l9+VjAB0zT4rq1/BrIJFpKDfVo4SPGyQjAre4ZsWIIYkIza3jCCPf4KdHl116SwXQIdZ7Q6GWWrYKrw4LsQnJZeMXOqEJQ86B0J3alqLPfNaCE95FLMyjQBU/iyeAQAN+m3bbxE7pjp8d0R+5ghQt3/CnR5bdeksK6uj8JMAGqbBAIWn0OvX8tRlHbpTkzxtqYXwVL3aJT4HWpYfvffqjUIHaNhv49rYEN6qzKFypMY1fB8nU4kNVYNX4dt8nLZfWuuSdIaRbxrCT6LBQ3gYB6l+DTuOboCCCHRr0Bu1AcqG9mrbaQPQsN/GlRpA7aN5BFAmaz2dAEoA3a5GAGizbFO/bgHooXgmFKCsy7t37eUHpyZAQ34b11YXkawKHsJh9crNTwIok+2fx/JJz1+ovPziAOhEqSHp2koP9ND8Yh1ECQNo1VU9lF3W6HugI63CG9F1UH4SQLks/9AqPMpPAug0GgxQ3xyoF6BaCE/fHKgCJY8Y6pkDzROg7tCXBFAu0z8EUJSfBNBpNHwb0wqs3hjr4l6AaiE8JQwrRtar66zjqQF0zfc4uVfhMwUoDPGoNY25A7R2hg6M2QEUTncKgE6ZGpKusfaBPuIbMMXOzI/uyJ2ZfoBqITxX7CtyH2hFytsszKc2hL+8X8gP61DLQb+Na7sA7X4SCQ8ihI9VO6cGfN5qISZQlPjUhsuMm59Z+UWuFzUQnTg1JF0DozEJmMljQqV5NgiG7mwmPOvT7DCEJzyJJF4//51mI734XfdVENCDc+wkUrwA7XwWHm0G+GpJj9Q0n7daiAcUXOjimsuMk585+WVRA3Th5ycBdBqNAFD3WfgWgIqz8H/tc3wxnZ2F/wu/JU6EXt5h5+LX9TYm+QtE5/PdIw5Q/qDjt1naNkC7RmPyzvUpaMwZoEGbE1z8zMgvCwDQhZefBNBptN2I9Ez6xXKhEUa7BQTdOkDxeKDOGo+YWSxMaGwMoP6GuQ3e2M7AzThSng1AfX5oN9N5bphkKxKAigviKoB6byfurQgAaptxdpDiAqgnmV3MBDwTbiYUoD1TMwuAIuVKAO2uSABaofPwvPzT30Lv1ByuGAHqnqLDzCxMbQqgvmR2MDMqbxBnjJqaVADq9UOLGaxcCaDdFQlAyzMxp/pzf4+95ZravGBhQa/eFm8cGzFAQUBRSxEC1LNIrJmxGkmXTsdwUHiTGW6GADq+mY7VQTeDlisBtLtiASiPmfzzv/Q8uGATWcpv1vslQJsYoHAZ31J8AN3Z8aEpGoD6kxlshgA6gZkhAMXLlQDaXdEAtHlphPmsAVoh8rPVAP8RX9CXAG1igMKAoppGCRk2uowa3Pq81VS6/8o+bumazM1oBGc0SqK64Orvh4BynaSx56cYANrsipKxlfEDRtrheQnQOgaoFlBU02jVdUztmGr9xnBk9HBL92RuRiPyM2WA9vZDSLmO28qzVXwAdR5xrz7Yf6C+JQFanyLVDtNb2uIQ3qqr4dLs2GO1MReRBqTSTGivlOipaTXjG7jOZghfdl6Fby1IomZnxQBQfQjvDrLEjzztv4aGbdLi4VkigLbkZ0AqzYT2SomeGqfUryKACikHOCOI6GZaC5IA2lkxAtQZ5pMtubOjn+cJARQ308KgWE4ihaFyc7zpdhKpd2qSAah+EincjKNcadzeXTEC1BPm8/LbN0VMERugIKCopegA2rK8Hc1Z+KCu5hYAOihMakYAhWfhu5jBy5UA2l3xAbQtzOflffa+CdBo50AdZnoAdBvRmKIF6JAwqTkBtInG1MkMAXQsxQdQfRUexAYVxz1lOD0ToFpAUUvpARTvUWjAmCtAlRMco9aZAVTGA+1ohgA6liIEqBbmE8QGrRjJ4odenAqw6gDVAopaig+g3iM+C+e9DOOmJghZrfzcLEBbl5znBtBeQ3g6iTSWIgSodhIJxAatTyLtv4UANLGTSL6z8PWqaqfDzT1SE9J62/m5UYC65z5HTE1KAO21iFTSWfixNB1AL99ka+bX7vqfWhW/pgOU/QfOwoPYoDJ+6JVb/6Z6hAcE1SZIxVn4VOZAS3c0pgUAaPjh5j6pCWrhrfzcFkAdvpkXQOEqPEVj2oImA2gddnn/Ld9j1qo5vCbZIf6IIyDoEg0oGiVAHeEqeVzc1n7WKKkJbOFeeoabaX2m/ZGFruYDOJE3PDXpANTpEKXGMbYZq1wJoN01FUDlhCULJo+FiHcrFKCG5AqTdtyzUaQAxVR3DOIBaOszGzPj4IXWmSKANoKOGSk1JF1TAVTty3QhzameABUBRS+XeEDRdAAKpqbaCDo/gC5Mibf16bw5AdThECXNMQTQSTQVQJuNm3JWEs5rgluU5EicBbMrrtw61+hYDcef3Cj2bgkkNgbgEL629K/FhMHeP8J6vGkAdGHdT+sdyBNAhVs0n80FoJYrfB1z7hgC6CSargeq9xLhyrq6x/O4VAC9V9SRPjWAvnyE3fcJANpYevq3f6769KUHp9gkaCoA3dFFANWFosLwGQFUVRbDMQTQSTQVQNn1Rrc+qH/UYnyKm+TPZOw6ftf73t2SRaWvPtUAyuZRz8CN88JAA1BoaSl30pvD+/GCh00vDKCjGU/XLUoWKtibJie6qkzYL6hDlAY6ZlweZKvJVuHZnvaiuPaagChyukgQD+7n5KeJNIDylytxdLMx0AAUWhI7ndbWCH686jq5dkyNyc8MAIrFv7R81tVmutXlsTcg6FDHjIOB7DXhPtBHHKHFS+elfr79Eo6y691IH7336o3CBCj/jL2jGagBqlkSX0xnG5NVwREtrFH9jmlmSGoSGKrqMnjR4r3eqUnHL90c0uYZomZ3TXsS6X22md6MsGRHlpfd1cICKIfh5ene64YBCVB9hX+pfxuKAOpJyCATmzWjb6QngOrh7AigW9DkRzkvTjjpQIxPDXJqKrPYu/byA2sIDwBqGlhBs1xrEBbUUIwARYQAdNLUpAMKKeQkkuWzeSwiKWkA1T4J/3vSKTUkXRMBFPQNxaDb1wNlsZbYViV7DrRTD/TpycE5PoJPBaBmpY/hLHwkoBDCtnaZmJgXQD1n4cf/w0KyNRFAweykNu2JzoEqDj49QedA2SGjgDnQ6vm9r5/iu/BTBejEqUkJFFxYNKaZA9QdjYkAuglNt5FeoQwuvGs/rFRYOgXQdaEPy+WxopVpwFqFl/+sixeP8HOjiQJ06tQkBQqmJh5og9C5A1TsH65fOv+yEEAn0ZT7QG+f8/NDcutnE+NT7N58dKS2MYkh/OX9wgJoc+07NGDuAxWWxP2eaISRZACqV/pu8cl6pCYxUCCXyok3tb858wNo7Rejdz76HxaSrYn3gdaHj7CTSIdlvZGe6+C+6l4KLYsv1yeW/CeR5IVJp4Xj3H0yADXOwk+cmuRBYRF0nNQk6hdrfnjsPywkW0MAWkesu3LLjOBRMe67j/4O/0geR3Kfhb+4WRR/lv8sJzj/mWTosnil6n6K52oDl29erx75J8VfNc7CC2NoKJGUAGpEY5o2NemDwiDoSKlJ0y+WWyga0wY0CkALC1y+kJ2oFX05vQao/fi66bhacv7ChAC60OOBos+EdimyA0VlRvOOtplpvNSk6BdHZLvGMY8DArsSQDtrGEAlNx8d9QiLrLQsDs/5pXHIJ06AomL7nfBPkgIo1g6gAteYAlKTHij0/jnqIQIoXnFCrhYggHbWKAC1Yi91AKhYgm9MAXUF6Jnzo3QAujBlPaHP+A1KTXKg0GeIcQ/NFKCtFSeg3hBAu2scgEpetoTsLOWtRtceAiOi26hhkX95WRwzgIodSmyatLh6W10dd4iZ/kMevASabhQ/QK3672oP+tLqsNSkBgot7wTQRt4aIxRSbwig3TUmQNtCdtazpnuwZ8lWfi5uwM6mBVA12XpoAFQz/cu26Vr5AHRnJ6QlBKYmMVAYeSeA1moHaFC9IYB211hD+OpVe8jOpyfFdb7hE47vqzev6HOoCqDs9UpQszJRPjpq+qphprlGDB62AVnNwPjcaAhdTKfsFi4HQAdaTbq6NGqpON3rzQAwzEljAPTy7AgcC/KE7FRP6Kvl60LEU65lAlQ78g4A2m6aaZLqOp068bMTQZN2y2M8WupwfuYCUH/F6VFvOsJgrhpvG1P7cfV6lRwuGbF7343970gPdP+B+rABaKvpRvEP4ZMExVkAACAASURBVGsZ7cCq+V51TE30Q1V/bjUPjZma6P2CmUEH8N1qDFGzu0YB6LW7JQjA5AyYJCYwueqBNrv++AEPPf/uV9QCkAlQMde5/xonYwPQNtNAqQG0GZ8SQAmg7Y+w7BNAt6Jx5kCZ2kN2srPqJuU4IBlFv9/0Jy2Almcv8G9dP9cB6jcNlBBASw2g5odtyOyWmrRAgQN0itSk5ZfSfRIJPBBSbwig3TUmQNt6oMgV8XLoXfVlrzanmWyAVg9++6ZYb8d7oC23z6cE0BIA1P6wCz9zA0UQP+cLUPssvPZEQL0hgHbXeAANmQO1dsaDSHbNZxhA2dfvM8a65kDxSMpSSQG0xCOMCxFANYBOk5rE/OKKxqQ/QQCdROMBNCBkpwr2sa57mxX41IWcsDvJv3wo3j/kEZXr32evwjtMQ8UMUHyk7oojQgBtAIr7bl4AFR5A44FqIoBOpBEB2h6y8+kJu+hdRvgUqrqet2Xsu5qg/Mv8g4tTuXuefe2CH5gXhAwxDRQvQPFOg4eMHfiZEyi4jLzjvpsTQJulxlYrIfWGANpdIwI0IGSnfKK43nzpnnhn79a9+hQR/7JYV3/+O/Ak0v5b4vXBeZDpRtEC1DFt5WsQ4fzMBxRKOD91380IoIsOAKWz8NNoTICGnoUHOhYf77Ev3VNb4MXyOtsguv9QjNj512TU0XePaoDapv/6300KoK6FU2+DCOZnNqCo5eCn5rv5AHQBANpyewEz015vCKDdNfm1xpZWBkBtdYiFZyqta40XCwcFLDN6vRc/DU9NIqDQzChPNOwwYvfPBqCu2uNg5GOKBzqFNg9QLi8jCaCGGbzvME+AKjX4NDwzc4A6+5kjpYakK0aA4scxg5QUQBemHGYcs1ezBqjBzxmeRMJrj3umkwA6ibYO0HVxfHZU7L9elo/YVvkrt75/WhR/vniFHT4SVyjVwUB53JAnN4o9eQUTuGZJWLnSrCnp2hZArUreR671UwIo6pnsAdqnroyXGpKuCAD6Il9XV8vxxS+eFPv/uLhaH8tsgoEygL58ZK6787V7YWX/RoYA3dlxtIrZAxT3zJwB6qwr46WGpCsCgIqt72sR0+6MbwcFN8JrwUCr9w8eajfFi/CjwIqFz2mCh40hq/6jTxmNov+vS8UtQUJw0dNSMtXFEFZ7Rqsrj4mlgYoAoGID/bI+kvQK+4HjcGmceC+XIlQIP3YEDz7VVpICaEvsT6EdU71/WzJuCRISqKqfoXSqi6FWfg4j6CjtPH9FANBmxeij9169UQiA1oc0tWCgkrLs29rR+9pKUotIbeuoARqWmriHqtgjTc69kf4GpiYNv/iH7zZMx0kNSVc8AOXHOYtCAhQPBirv6WQxnLTgTwTQXqlJAxRAIOcEUA2eBNAtKQKACuSxtaK9ay8/ODUBCoOBagAF4UdrK2kBNOQkkhsOM1xEgmi0iTFaahLxiwZQ9ob3DwkBdBLFAlAWdum8bOZA8WCgnh5okgANOQvvbBMzB6hF0PFSk4pfAEDFG76OOAF0EsUCUMXDpycIQEsVDHSpbke250DTBGhANCYCaCOdCf9/e+fba8lxJ+RjNvZLL8uGvD7a3bsCIUbDvDFZQbQJLGtdslKIRxk+AAgxY1ZREhzkLOzyGg0OeI3AG9js+yMdG4iFREj44w/ADTOLkCYWN943MyEa654vQFdV/6mqrurTXV3dXdX9PJI9555zbp06v6l+pqqr6lebF2grGxMCnZ3UBHpd9jRrgRrJQMvZ+fLeqDELn6lAfflAm+cRaAMC1ZANRLUSBLoYqQhUDeGLfqYtUCMZqLY+1Eg/qgnUuQs0ZYG6aA6VO/gzOW5eoO7IbESgxtCl/vZ+fyLQaUhFoPJQj4I77ytRNkN4PRnocf/VJomTuROpmYpyKDRjgZoGjVubHERhYEvBFZltCNS8ed58fa8/Eeg0TC7Qaghu0hbo6fHfk7k9r7V7nVd2MtDj/pv/63PiXTIfqLEXvixFZgttfVzOAtUNGrk2GYjCpGWFoDsbK4iLtXzDtWpjotqAyUICdXHlzg6qUwhUOxOpP5kJ1FweqroYIVdE/qI4X8wU9/qSj4vdQIxiXG2lLMbz0pDagElCAu1BLdCBZC5QeqDzFpNOTTzFdArUW4y3czqkNmCCQMt3JCRQ6/JwZr2MUpvkRbFQMenUxF2M3UAOvYrx3x4dUhswmUmgZSLPb8iblLfePrmeUcPym/fEUUjycStV6JPj/m+qhHXlEF67C3rUU4jaJCbQ1gXQiTGz6ng9vDapi2KpYtKpSVNM39biLaZjgn5IbcBkNoGqRJ73jvvyAOP2M1KK6jDOi99WArVShb5iCVSfhz9qKURb5CzQ3e6MQcNrk6IoUigmnZo0xYwVqNWMgmsDJrMJdH/3iVjlefFGs6TTekZK8UqkBRV5Re55UoVqk0hGRlB9iahBpORhM9Ip0PBicw/LRGTYXFrW7PE7g5vRVEJYGbMJVKiwMJ7sOO7rTUX6M0KK5QZN9bQzVagmUGMvkpZC1GR0c52fDn+SDzQ2OTaX0f7s0Yxii2ClzCZQ4bVyv6a21FN/RkixOk/uqATqSBXaCNTcDa+lEG2T2BC++2VzmN5q+T3uZfWtTYpD1RSKSacmnmLsYbu7mO6m42hDWHM4iQm0mmO/MgSqpwptBGrmY2rnH9HITaCaQxGoxtlMn3Fqk3xcbIG644JAZ2C+WfiTLVD7GYdAHalCdYHqGUHXI9CT0Qn1t/cNLmNCoCXWvNH5uPT6pxeBDic5gapOpy5QM1Xo+nugJ3MU7+8vjK5N+qKwQKAV5rx7j7j0Gbog0OEkJlDrHqgjVaj/Hug6BUo6Ow0EWqP7E4EuR2ICLaVYuLIt0Ot986RrFn5VAiUfqAvrwq9CFLs2ecRF+/Zmpn5XVBDoRCQmULEO9J1mHagjVaga5GvrQMuMoKsTqPaDu+FvXaBGHyxqbXKOiycq7ESaiNQEau5EcqQKVRk/XTuR1ipQ9sLXGIfKHdyu2LZAfVE5sRd+GtISaJU8WS1ZalKFPmhShaqMn6698CsVqD6PZFwU2xaoNQ8dtTb5xsUblRPZmKZhoYz0HkyBxiM3gWrFNFPx9kXRdSn0q012omiKORx8rhBvOROa1cbFH5UmLgg0KskJVK7trFcoRSJ7gbavibPdiR61yVYU3QId3dPKNi7nBHomNAh0OMkJVE4YHd1nwwWTr0APNuXz529o9ahNtqLwhuUU5V5frnHpiIrRA/WFBoEOJzWB/gU1fq+yiOg5PuXZSF8qj0AqM4U22UObJfiOm6D5CbR1LZj0mFLtUZtcRdGpigizzbnF5UxjOVTFnAsNAh1OagK9859fL/RZKtPI8fnUOIRTZQrV5uzLjZ316nqDtQl0t+uhifWJoo9A+4RmbXHpKdCzoUGgw0lOoPrQXc/x+fz+/q5cDSo8WWUK1bOHVtP6dkbl8cnDlqR1MchnrUuhX1HrD4tgcGhO64mLPyqSYaGJf3WvktQEWk/CC0HqOT6vzDQjcoxvZQ8Vb2gfmTRZc52HHv7sadDVh0UwPDRrai5D/HkuNJNc4OsjcYHWOT5vHpbz8rKTWt7wNHbOq77n0bn8Kb8hfP3QuiZaV0HXoGxtQ9WOWfjOuNixWW1cXEP3AZHBmsNJTaDWEL5eHK/udlZ3RK+rsz60Xql0Z3sEL8lNoL4F41sXqHPHDQKt44JAZycXgT6/bwpUmtMUqPjJc+hxxgJ1bS45a86etclWFCfvnpteoVlvXDxRkcWcDw0CHU4+AtWX1tcC1bKHFm+588Q9gs9aoK7tzf38uWJRnLy7vvuEZsVx8fqTWfhpyEWg5vKkUqDGPdDij4vff+gcwectUEeCHQR68uUd2rhA/dmYEOgk5CLQ4n/qJdnrbBLdNdlD1epQ5wg+bYE6WrvdukO6WT1qk7MoBC5P7FTSgM0KtFKooxgEOgHZCLSw5J0PTuWi0ObmZ5M9VL6ldaixImGBesahZ4ro5c+Vi8JDHVB/aNYdF989UHYiTUFqAt3r65iMFHVl8s/93VMjUD17qPzJk4MkXYG671kNOiQsvDZ5i8KNFtBNTq515kk902oQ6HCmEmi/xHRXVo+xS6DlXvi3xcN6sl3thS/fV7zxL/3ru67PSVagnlnTIWfcjKhN1qJw4wnosNrkHBdvAH6s3jjm1ga0SUuggVRz71f7v5XXMqbDwd3e1baRM8Wcf8vqROEvpgyGL6DDapPCF2ro0RT8GwxatRlzzxzaTCfQuAnpPB9iZA+9eXjxL9ch0D7dywi1SUsUI4qp47U+gfZrCv0FOq42YJO1QJ/fN7KHPtq/ktdC+oNN+XyvG5wItEaLVy+DZhSXnk2hKsbXoiLVBlrMJFAts2dzw/KVKjNImdzTPOKoSQVaPH76YH/xDXke0q235W+rG6L/pswe+s+Kt/6V/f5X/q74KbV8oK1GfQ5jstTvAQRa0o7XagR6bt68KaarQcWqDbSZTaB1Zk99DWcpUJXc0zpks0kFetx/Tb5y71jnWq6Sg/7918Uff7X06D98uAaB7nYtI0SszbASchCoI15rEaj11bqKQaDLMJtA68yeZb4Pef+yFGiVW0kc837zXnnMe5MKtHh894k4F/7ijdOzh+Wi0CY5qP5WxxB+uuRho2m1dPmsddVE+7RswjKQLoH2+PWUm8vgpuBuUUHENsJKmWEW3s7sqebMy+1FTXLPOuOnTGJn/oJ4rLJ+Vicga1lE9LfmJdCIyT57kE9YBtGK10B9JNxcAppCNH8i0H7MJtA6s6eSnNqFWQpUCrDe7V4eG9/8wrHMFVIfIW8mB22V3SbRSaRBuepaI7ltD+H94Tk7gk81Lj3/+tstgVn45ZhtCK9vyiwG3moFki7QOt+SfF3/BYdA9dx2+lsR6JDaZCdQ7TYgAtUE6s+TGv1LgckCApX9RzUIv9KG3XUuZIdAVcqQWqBmctCMBTom2efGBao95xVoaG1SiUvvptB7J9KY2oCLBQQqtFmO102B+nugLYHqe95zFuiIZJ8ItH5yoD/ziUvfptB3L/y42oCDJQRavPa/1R4iXaD2PdAOgZrJQbMWaHiyTwRaP+kWaHhtkolLiEA7sjGNrA04WEKgxRj+29XEzz09N50xC98hUDM5aN4CDU72iUDLyFnx+rEzTeig2iQTlyCBuvOkItBpWEKgxR9lhhFDoGod6E/fKteBdgnUSA5qCtS5gzRpgVpYc0lT1iYZUfQtxt3TMuMVoTbpxKVnU6AHuhhzZGNqJaYrXlQTRoZArZ1IXQI1koPqb316uXcpNCOBWkaYtDbpiKJnMZ57fUa8ViXQnnvhuQe6GLML1MhzVwtUSdTYC+8R6P/d//kn4vlfq5ODGnL+0WXeArWMMG1tEhJFv2J8s816vNYl0H7ZmJiFX4zZM9K7E4V67l06uG7WP/UnG4EaSrCP641em5RE0aMYPQYHE+215AWqf40+xZyzp1GMHZeztXEXA31ZQKCuu5RDBTqQ/ARqtn9nN2RjAjVjMKkopvxC5teIHl4EOjMItHxHEgI92Kin3TfCtiVQMwaeQEWqzYRfyPqrjB3eaeMCbZYX6KPL/cUb1/bSpdPp8YP9/tYb6j2PRda6W19/Up4i90o1hPckEG2RhUBbjb+6ADxTsZsSqBWDXAVq/1XGDa+vAQ0sBgawuEBljs/9V1sCvVK3SeUz75X3TF+xBOpLINoia4HudvZlF6s2+QjUjkGmAm39VSLQ3FlaoNciyefNcW8L9Onl/itPTo+kD4v3vH0SJ3aIaXhtEsmbQNRgREav2Wk1f/GkddUFF55vWNoxcAYqjDmbS6y/Si/TxQXcLDkLr/LcCWnePLQFWo7jj9pKebWBUxOoN4GoQbzWOQPn/Rl+2WUclnYMonliToFG+6v0M1lcwM3CAq3SglxZAjV3uxf89I+//WBvCtSfQLRFFkN4gdX+W1eczbjaJDqEP/utd63D4wbdGp4zLn2+y5m/SWbhU2bhIXyVw86eRDLzLT170CwcbQTqT9/UIjeBVoJAoAh02AYDBDozCwu0PNndIVBtMkh0Wi++9LXvPbQF6ksg2iIbgdY7kZrm773UNjSJ5IiBzxOJCdSg/TUm3qE1sBgEOpwMeqDiBmlz5se6e6DlBWCcxenrqmxIoK4YeDyRskAnm4VnL/xiLC7Q8/dAq/c8v999D3QdAj3VAq2eQKDuGLg9sXmBko1pVpZexqTPwlfZQrVZeL03er1vcje5ZuFXIlCV0E77EYH6YlBawpTF5gVqB6T8CYFOwtIClYcV37y3V8k89/Lg92Yd6GOxrFMN4cW58M0RdNo6UEcC0RZ5CdS+INz+3JRAOxJj2h2ulAU61U6kjjmoOjwIdBIWzcakVFjvRFLbjH71P+o7kcTD6/Ld7zcZP107kdYqUF9+0E0J1JsYs3XLL2mBTrQX3i9Q3x1R9ych0MEsLtDToy9We+FPN/98v7/9wXVrL7x4uP/1svMpM3669sKvVKDqnqjDHdsSqCcxZnvSOW2BTpONyStQ75y8+5MQ6GBmF2gnxuolg2Nri+YQchOo2rQiH5aNX/xsXQQbE6grMWYVFV0RiQt0eD7QwNrYselhUAQ6HARaviMpgWrdFP81sDmB2sXUUcpLoLMU044NAp0EBFq+IyWBajfKDjYxa5O3KFxRilObvOMi8LSgCLUBk6QFWqcBlQJ9/MX9xdfVDL7n3uf1/t6jy/3td1rl5iVQY6rWb9CNC9QZpTi1yTouAn8LGl0bMElZoE0aUOHJr9aPvbPv1/svZ38qZ2uxIAJ1FuOOUpza5BwXQUcLGl0bMElYoEYa0ONenASv0n1aeUB1gTrsGS1T2Fx0CzS83MzDYuGOUkBBuTcXB94WNKSQ2Nf2SklYoEYa0KNKNX8lDOndgXS9v3AM3zNjZxPJn+sSqDtKISVl3lwceFvQoFIiXtZrJmGBCuo0oKUnxRv8e+CvXcN3QcJD+FZr7xbobpPZmErORkk82O4Q/mwLilEbMElZoHoa0HIW/ubhxTv+LEwIdFhtchMFAvWgGgUCXYCEBWqkATUE6ssD6j3yOGGBtugUaNzaZCoK9cgRpVi1yTgu6id3C4pSGzBJV6BmGtCePdAVCLTrBMrItclZFD5/ItCOFQoRagMm6QrUTANaelJksO+6B7pGgYYneFyzKNqSiFebnOOifnQINFJtwCR9gao0oOWhm/qJnWoWXk8iulKBGgkeh3S11iwKR5Q8xTj+6VlvXOSXdf7jgkAnIV2BmmlAy2Pf98060DIPqJ5EdB0C7ch9aXRGty1Qb5SsYlzd99XGRZ9rtGODQCchNYHWqe6+aaYBrXYi3RVv03ci6UlEVyJQb+5L83boxgXqi5JZjPMG8lrjYq7WsGKDQCdhXoEWtrvX8fLV/rOaQKuMoOWtzs/98uf2v/J3pED1vfCnm7fqJKJrEagn96WV3XHrAvVEySjGjFjf2mQaF+PLtmKDQCdhXoE+vfSt1JRcdehVju69hjzDNOZrGy6ehx1l25ntViZQ1zc+V4zrd/S3WBHrW5uk4qIX44uRLMb6st098+DagMm8Aj3+6j9wbLbsRXICdY8fp+zIrlmgnj73yNqsS6D+GLkEOlFtwGRWgT6/f+dRoAKTE6hnBmNCgR5swooZVJG5ROG96zuqNr6I5RMXvZiOGIlizjUPBDoJswr0en+vmWcXR3He/uBKnbSp8niWQ/jmNCRtkWfxi//poUpo504B+uyty/3+Sx84Pzi+QD1zwBMJtHVx9JNoNqLwRHNsbVYlUE+M/E3D7nbHqQ2YzCrQYzF+r1LLq9nzi99WAv2ydtZmeR6neNhHoGUK0KfazHyL6ALd7dztGYGGFOOL5tjarEmgvhgh0GWZU6DySPgq4ceVyPYp0oXca/J4Vqe9f0Ws+BQ9VVOg1RDelQL0+f39XblqtHUmyBwZwyb5DJvWpTHw92cISyBTRbNXxOZoLhE4G6OxzcNmAgGskTkFKv1YbjAq92MWtrzX5PGUbyj3GR21DZydAq1+9ZXmMwxGtiMnO5spPqTFyAtk+rAEMl00+0RshuYSgR4xiutPBNqPGQV681D2DpX+ZG+0+qnpld471Tvd61fPCPSOyjhSzu5X5ZqED+FbDXcA3s8KrI19hQR/qf4VmWaoGhzFrczCBzU3z5cdXxvoYEaBPr1UBxrJUfa13mPUBVrnWhIMEGi9At91rmduAnX/LgI9v1IUgSLQWZlRoFf6LiNLoM0PrlM9OgUqf1XbAxpXoO5ivFd5tJ1IzqfNCyRfgVr0/pdnsEDXsxOpO0aOnUhT1QZM5hOo5rhXzE6nJdCBPdBSoJ0L9HObhfeJwrhAViPQ/rPwgwW6nr3wnTFq74WfrDZgMp9A60XwTy8L2Vn3QBuBDr4HKn/V+K02axHoSrMxTSjQ1WRj6iNQX+bYmLUBk/kEWi0AVRlFSuMV3VJLoNV8upb5Uyb71ATqSgF6VfZo3cciZSbQw27nXeDZXB4I1JXrs12b9ntyiYteTC+BVgqdsDZgMptAtXub5dnERT+0XgeqCVStA318qe6U1sk+S4EKPbpSgBYmvvPBqTw4vkV0gU65E+kgEkH4LoP+xfSoTTKi6OlPW6DODleEL5VMXPRiumJkC5R7oLMxm0CrhZonqchvWjuR9EWcV/WNUj3ZpxToU7nryJkCtMwRqhKG2sQX6HR74Q+1QM8YdEUC7bsX3nzVfctvrQLtilH9TMddUAQ6CXMJVL9JqQbfai986x5owaPPVXvhT//vb1fJPlUP9kcyH16dAvT5/V/ev3Lz74U0j/tfE3vh33Z++gQCnSob00ET6JmNmuc/KB9R9PKnKVDPpPNqBdoRo9Y90D63NsJqAyZLZ6Q/Oobc2mjfPCje/U5jZsnNFAJtGvTIYgwOh66LoHcx/WqTlCg67Fm/pBfji1QuApVfamAxvhiVxXQ2HgQ6CUsJVByvefIsP8pFoI4+AQKNX4wW5PUItF+Pe2BtEOj8LCXQ5/flkXFH16R5NgJt35UaK9CDTVgxPWuThUD1IGvFeCOVhUB73vMdVpvuxoNAJ2GxIfwjNefjWv/uEKiR7PPx68Uv3vr6k5OV4+7pg/3+NeeJIdMO4bXrYJRAWxdAt0O3IVAjyGsRqKPljK3N2baDQCdhuXugQoMXr7kSILcFaiT7fK/Zz2QI9MvyTc4zlyYR6G7Xvg4QaORirCCvQ6CuljO2Ngh0GZaeRHLREqiR7PNaJBIVPdii82oM4cVCUMc60LFpvbzY13YMWldAlFIVM4UlLt4gR4vUXM1FY4qW8+PYjWeKK3uFpCnQfYNwppHss7zZKddFGQKVfc/2rdBIrbPFziZKqdP5M0uBdgQ5VqRmai4a07ScH0duPJNc2usjB4G2k33+9I+//WBvC7TZAdoicLTbaunh9K2NfwgW60sNKmH5rENefJFKcggf2FIG1qaz8TCEn4Q0BWoO4a1kn2L/Z5kVz5NhpEVuAq310FnlsC81qAQEGliEVUxgS+lbm3Y2JgQ6EzkI1Ez2KSaULr70te+1hvDxBdpZjLPlsxMpbjFdevHIIkmBGpx35tDasBNpMfIQqLbYSewDLXPQLyvQ+LPwCk2gY4rpU5v0BdqSjf6aWxbpCzT+LDx74RcjB4EayT4rmxbd0nUKdIvZmPzFdHbWnLLYtEDJxjQ3OQjUSPZZCfR6X53/sTaBqnyg9EAlZ0a7CLQssHmsx0N/jEAnIQuB6sk+1RD+5v29JlCh1/kFGn0nkl6ytyMxpJilRRGjmDOyyWQSyWKIP4cKtMGMDQKdhCwEaiT7vFYP77xvJgldQKAT7IWvC/YPxQYUs7goYhSjB9nXA43c05o+LgP8GSxQKzYIdBLSFGg96/4XPysFeqUn+3z8YL//c/t7ajwvbpH+q7+8kEAnyMYkOeiLdMKLSUAUMYrRgmyLwjXrnIVAI2djchVjxwaBTkKKAjVW0pcCtfYX6RuOrp3ONJhKoK0MjVEEKsrsmE7tW0wSoohSjO8W8+FgWyJObWaJi/xSIcU4tOsophUbBDoJiQq0GsM/vnTbMRmBxi+m7psgUIXWWVuTQAOLcXZdEehiJC7QKvGyzXoFqt0dO2PQjQhUv11oFnOwiVWbhOPivnnaLqYdGwQ6CakLtFrHJHypTlGqMoqU2T9vynR2p2ciS+hvveEsMBeBihWgDoO6LboNgRoB2bxAjWjYxbTi4WFUbcAkdYFe72uB6ud4Ntk/K4GWKUPdvdFsBLozQaBWQDYuUCsaVjEIdAHSFujNI3UPVAr0SiQCLU+S17N/yiF84dGvPBH3TFsp7uNkCpsJp0AjlZ1lWGyB6rTMEPQJOTWXrmiYjI/NVBf3ykhUoM0svNyDJARabugsepr3zOyf1yo3vetwEEF4c52fnU1Ef2Yp0FZAjFdj+DMngXZHw2R0bOJf1qskcYHeekPu4RQCrZKBHpVAm+yfVQ/09ve8BaY3hG9dC25E2289Ga02KQ5VewZGxsE5NF3JEH5AHJytoxUbJpEmIVGBiiH8zfv1kXNCoNeOtPSNQIuHBbf/hfNMOQTqr8jIIqIXM0QVCNQVFQUCnYeEBSqlqNIw9RDo6dEX1W7P2Q6Vm6AYl0AnrE0KouguxqeHEoc/1yJQVzHeaLATaTGSFmjhycqk98qcIX6BFl3W//C6TDHSJheB2pdIlz83IdCe2Zg0f65ZoN5ZePbCL0baAn1+v7rX2boH6hDoSQ77Xeca5yvQSWuTrCgazgg002xMocUMEijZmOYgbYFWg3htFr5wqkug1Y6l+tw5g1wFarxmaCJKbZIVRUNLoHYUAsKSb1y6BdoKDflApydxgRbWFEKs1oG+06wD1QV6R25IEilDnz2cNRtT/GK8/gzpamUrCo1Wn9yKwhS1STcunuYhHrcbyCS1AZPEBVouCphvwQAADQNJREFU+3TsRGoEqvKBVjuRbru2zucjUF+myKCbffmKQkMPSJ88qasWqKd5mCsSJq0NmCwk0Ep36nRiG02gah5J3wvfugd6+tGl6IM+e6so89bXneuYMhKoO91O2HRzxqLQ0IbvffKkrlug7uZhHfI8ZW3AJEmBdnIc+gunvASqJ3ysHh0OjgtkKwKtw6CHoHWHOGZt5o2L/6u4i3G83953NKI2MIzFBOqa6znzK7Jb6t+z2UFeAq2KMWdONizQElOfrW5YvNrMGZeur9K/GAS6FPkIVCxpenK6OTrXKZ0hS4E6pk6MC2RrAnX406WdvATa+VV6F2O3D5dBEegkpCJQcdDRrTKZ57O3ioH9ax+Ih8V4Xewwkj88UiP+X/pH5QtPH+wvviHvgN56+6Sts3dMw+coUGPC1XWFbFagRmSmqM18cen+Kv2KabUNt0ER6CQkItArJUc5M1QewXkhbnUe97/VnIz0Pz+rv/A1+bZ7R/nUO9XEU7la1CJDge5MEGgtCisyE9Rmtric+Sr9ikGgS5KGQJ9eimyej6Qni8ev/R8x4y7udRZ6LMbtKu+n/cLdJ+J0+Is3ysWfx3KdvX0ESHDysGXpFGhQibmHxSPQscWeFoxLrK/S8meEuk1w1a+R5Wfh1UJ5Oe6WDqzG4EdlxSbvp+MFtUpUDd/Vjs72AUkR2tIC7GzGXx7Zh8Xpz9EGXbC5xPsq8f2JQPuRhED1cXf9WFpRy/tpvyDMWU7Jy2dU39O9yCn9IXzrWuoSqBrtbWwIX4XAG6F4tZnqC539S3Z+I2bhUyaJIby+NKl+LIWoLZh3vaALVLqzPYKXINDeFRlYxHzFiBCo/xBou2AEuhCJCLTRnpaJyRao+YK646kJVIzePUccpy9Qk26BRqtNVgI9nU5ugcavzVxf6NxX6V0bdiItRiICDeiBtgT6/P6dJ55tSvkJtGMWPl5tchOoEsUZ6WQk0Diz8Cf2wi9IEgLtugdaC9R+oSXQ0/Hi9x86R/ArEOhmszEZIFBfOWRjWogkBFpNsMs/rMn2e74X2gK93n/50jmCX4NAN5oP1ET+Q4JAHeWI/3fYs18xCHQ4aQhUrQN9rC33/Olb5XJPI22d/kJboOIwT9eBHlkK9PwmlW0K9HQuMjkJNMpOpJMvI/3gYhDocJZfxlQuBJXI/qO5E0lLW2e+0BZoMcj3JBrJUaC+zKARa5OpQLsjk5VAI+2FR6CLkYhA/XvhNYFam+RPtkCLt3kSjWQpUHfqx5i1yVWgnZHJS6BxsjEh0MVIMiN9KFeeEXy2dF5Ym2ZFkVnRV9kga/qru3kYkCoUACCUNQn0kXsVPQDANKxHoMe9bwoJAGAS1iPQ90VmOwCA+ViPQAEAZgaBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkBLHr++31+oXKMjeX5/XE6Tm7cuq6ynC9dEQFzcEBcQIFDFeyq5c4x0JMdxSaHEwSQF7tPxZq2JgLi4IS4gQaCSa5mJ5NnD8e3w5rgf1wyP+zsfiJp4kuvPWJMTcfFBXECBQAXVkcnFv+bOc+X7I44mGdUMn17Ka7I8rGTJmpyIiw/iAiUIVPD8ftmTOI48FORqv787Lq9zdXjz2ONJxtfkRFx8EBcoQaAGoy+I228Xw7sxzfBY9mnGlRKjJjrExQ1x2ToIVGf8QOg0sinX5zo9vRx9UyveBUFc3BCXzYNAda5itKAVXhDExQ1x2TwIVOM6yrKUWBfE+IUpsS4I4uIrh7hsHQTacH15MXJOVRWzth4FcfEUQ1xg2wK9Vsuh1WVwFdyfMIpZwwVBXHy/SFzAAIFWLfm98PFYxAsi2qzqqDKIi+8XiQsYbFugDTfH/e0Im+EE45phtZ5v7Lq+8TWREBc3xAUkCFRxHL8VrmJcM4y2s2R0TSTExQ1xAQkClVzFux5GNsObh/vbcfY2x7ggiIsb4gIKBCooM9rso+wGHtkMn8XLrjP6giAubogLlCBQwfU+nQvi9OytohqvxejgjL4giIuvAOICEgQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKABAIAgUACASBAgAEgkABAAJBoAAAgSBQAIBAECgAQCAIFAAgEAQKabFLsUkeDkvXANIkxdYKWwaBQkak2FphyyBQyIgUWytsGQQKGZFia4Utg0AhI1JsrbBlEChkRIqtFbYMAoWMSLG1wpZBoJARKbZW2DIIFDIixdYKWwaBQkak2Fphw+x2CRr0cMCg4CS9xgobZlcRtdSfff5P/dGIXz9UDPu1T/7tp+2n3t29Wj/+/u4zqmLigevNkAMIFNJht5vEoOMEejiEGfSj3Uv2U36BOt4MOYBAIRl2u2kMOkqgh0OgQc8I1KwYAs0UBAqpsNtNZNAxAj0cQg2KQLcAAoVUGCjQj79VvOkXv3sSXvqMfOb70kIff+vl3e4XxPOFlj7zg5d3n/pd5akf/rXiF37ub3wof+PVn3xh94J83M1AgX4sPuNP/+bp9Mmb4ju81PrYH/58+bH6EL5880e7Fz9svgfkAAKFRNjZdL/9o5flm154te6+FRoqfvhJ83zxwp8pfnrxf0iB/kFZrHjvu7vfeLl63MnBpvvt5YcX5dYCNT/2r9ePHQItO6Tqe0AWIFBIhGECLVT1i//99Mkf7F74p9VI+CcvF3/87PO7T394+uQ7O/HURzvZp5Ovf7R74Z8Ub/qB+IXCZLsXv3sqeqfnTDVMoIX6/mzxeT98WXyG0rr3Y12TSGqIP3LRAMwJAoVEGCbQapj7rvhTiUc+VT1fakloSxmpHOer7t270q/Fm148M4gfJtCffV5+nqJyoudjXQLV/w9ZgEAhEQYJtB7myvuGUjnyqU/eLBX2k5fl81KQdZfuT/7r73xhp0wmtXa+rze4B/qpP6x+aDzo/FiXQFV97KkmSBgECqkwpANa9/Wkc+T/5Ahe3U2UyGG7JtCPv1C+8GrtqFq3foZNIn1flP+pfyy7taVAfR/rXAcq3sAIPicQKKTCMIGWlpEPSiO9JH40BfpS/R4xv/PCL/zGH745pUBPP/h5+dmf/rByovdjnQIVfzCCzwkECqkQ3gMtZ7Jfte5C6gItXn5JdAw/mVagRZn/Tixbqpzo/1inQH/2+Rc/ZASfEwgUkqG/P617oOKO538TI3hrCZAm0MqsRRe1uRkpR/1nCFhH/8l36huz/o91b+V894Xfe5MRfEYgUEiH3v60ZuFFn+531BPVxHozuWQK9CPZO3xXvavXivUB/qyEXM5gaQJtf6xboGLlKiP4jECgkBC9/VmuA/2Tb6mFSnL2RnY9i67ei989lWstW0P4om9Ymkz81GMdqKB//7P4EPHhH78pPlcq3PmxO9c6UOV9cQ/3Mz3qBImAQCEpetlToO1EOkmfliPf8vmdSA+nTyJ9pJ5+8Ttq4ajaEtTTVb1H79VOpE/9kXr84ofOjxV1MwWq3iwVfPa2LCQEAoW06J1FpNkLf1KTNfXzYi+82P5jCPT0wy/sdj/3m6qv9+7u1aL7WfzUk95ZROSHq43vp//ysvio9se+ID/WFGj55j5r+yElECikxSwJ6YfOdM+XkP77jOCzAoFCWmxboD0WVkFKIFBIi20L9Aesos8LBAppsWWBvrtjCikzECikxZYF+p1yggmyAYFCWiR4qjHnwoOPFFsrbBkEChmRYmuFLYNAISNSbK2wZRAoZESKrRUAIAsQKABAIAgUACAQBAoAEAgCBQAIBIECAASCQAEAAkGgAACBIFAAgEAQKABAIAgUACAQBAoAEAgCBQAIBIECAASCQAEAAkGgAACBIFAAgEAQKABAIAgUACAQBAoAEAgCBQAIBIECAASCQAEAAkGgAACBIFAAgEAQKABAIAgUACAQBAoAEAgCBQAIBIECAASCQAEAAkGgAACBIFAAgEAQKABAIP8fZrsG5ZikvH4AAAAASUVORK5CYII=" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co">#dev.off()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="appendix-a9-models-underlying-figure-a8" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a9-models-underlying-figure-a8">Appendix A9: models underlying figure A8</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>dc2 <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(topic <span class="sc">&gt;=</span><span class="dv">1</span> <span class="sc">&amp;</span> topic <span class="sc">&lt;</span><span class="dv">26</span> <span class="sc">&amp;</span> topic <span class="sc">!=</span><span class="dv">21</span>)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>dc2<span class="sc">$</span>attention_others[<span class="fu">is.na</span>(dc2<span class="sc">$</span>attention_others)] <span class="ot">=</span> <span class="dv">0</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>dc2<span class="sc">$</span>topic2 <span class="ot">=</span> <span class="fu">factor</span>(dc2<span class="sc">$</span>topic)</span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>dc2<span class="sc">$</span>topic <span class="ot">=</span> <span class="fu">as.numeric</span>(<span class="fu">as.character</span>(dc2<span class="sc">$</span>topic))</span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(dc2<span class="sc">$</span>topic2) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Economy&quot;</span>,<span class="st">&quot;Rights&quot;</span>,<span class="st">&quot;Health&quot;</span>,<span class="st">&quot;Agriculture&quot;</span>,<span class="st">&quot;Labour&quot;</span>,</span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;Education&quot;</span>,<span class="st">&quot;Environment&quot;</span>,<span class="st">&quot;Energy&quot;</span>,</span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;Immigration&quot;</span>,<span class="st">&quot;Transport&quot;</span>,<span class="st">&quot;Police &amp; justice&quot;</span>,</span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;Social policy&quot;</span>,<span class="st">&quot;Housing&quot;</span>,<span class="st">&quot;Econ. regulation&quot;</span>,</span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;Defense&quot;</span>,<span class="st">&quot;Science &amp; tech.&quot;</span>,<span class="st">&quot;Trade&quot;</span>,<span class="st">&quot;Foreign policy&quot;</span>,</span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a>                        <span class="st">&quot;State &amp; admin.&quot;</span>,<span class="st">&quot;Domain&quot;</span>,<span class="st">&quot;Culture&quot;</span>,<span class="st">&quot;Reunification&quot;</span>)</span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a>runmods<span class="ot">&lt;-</span><span class="cf">function</span>(<span class="at">df=</span>data,<span class="at">topic =</span> topic,<span class="at">cn=</span>cn,<span class="at">countryn=</span><span class="st">&quot;Country&quot;</span>,<span class="at">output=</span><span class="st">&quot;&quot;</span>){</span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a>  df2 <span class="ot">=</span> df<span class="co"># %&gt;% filter(cn==&quot;BE&quot;)</span></span>
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a>  reg<span class="ot">&lt;-</span><span class="cf">function</span>(x) <span class="fu">summary</span>(<span class="fu">feols</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others<span class="sc">|</span>year, </span>
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a>                                <span class="at">data=</span>df2[df2<span class="sc">$</span>topic<span class="sc">==</span>x,]))</span>
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a>  df2 <span class="ot">=</span> df2 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(topic) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">no =</span><span class="fu">n</span>()) <span class="sc">%&gt;%</span> </span>
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a>    <span class="fu">filter</span>(no<span class="sc">&gt;</span><span class="dv">20</span>,<span class="sc">!</span><span class="fu">is.na</span>(party_share_last),topic<span class="sc">!=</span><span class="dv">99</span>,topic <span class="sc">&gt;=</span> <span class="dv">1</span>)</span>
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a>  tops<span class="ot">&lt;-</span><span class="fu">unique</span>(df2<span class="sc">$</span>topic)</span>
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a>  tops <span class="ot">=</span> tops[<span class="fu">order</span>(tops)]</span>
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a>  <span class="co"># tops = tops[tops&lt;25]</span></span>
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a>  mods<span class="ot">&lt;-</span><span class="fu">lapply</span>(tops,reg)</span>
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a>  lm <span class="ot">=</span> <span class="fu">length</span>(mods)</span>
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a>  <span class="co">#rowse = data.frame(matrix(nrow=1,ncol=lm+1))</span></span>
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a>  <span class="co">#rowse[1,] = c(&quot;Year (election) fixed effects&quot;,rep(&quot;+&quot;,lm))</span></span>
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a>  <span class="co">#attr(rowse,&#39;position&#39;)=3</span></span>
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">names</span>(mods)<span class="ot">=</span>tops</span>
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modelsummary</span>(mods,<span class="at">estimate =</span> <span class="st">&quot;{estimate} ({std.error}){stars}&quot;</span>,</span>
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a>               <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;party_share_last&quot;</span><span class="ot">=</span><span class="st">&quot;Party share last&quot;</span>,<span class="st">&quot;attention_others&quot;</span><span class="ot">=</span><span class="st">&quot;Attention other parties&quot;</span>),</span>
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a>               <span class="at">statistic =</span> <span class="cn">NULL</span>,<span class="co">#stars=T,</span></span>
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a>               <span class="co">#add_rows = rowse,</span></span>
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a>               <span class="at">title =</span> countryn,</span>
<span id="cb14-33"><a href="#cb14-33" aria-hidden="true" tabindex="-1"></a>               <span class="at">output=</span>output,</span>
<span id="cb14-34"><a href="#cb14-34" aria-hidden="true" tabindex="-1"></a>               <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|Std.|FE|AIC|BIC|Within|Pseudo|Log&#39;</span>)</span>
<span id="cb14-35"><a href="#cb14-35" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb14-36"><a href="#cb14-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-37"><a href="#cb14-37" aria-hidden="true" tabindex="-1"></a><span class="fu">setwd</span>(<span class="st">&quot;art/&quot;</span>)</span>
<span id="cb14-38"><a href="#cb14-38" aria-hidden="true" tabindex="-1"></a>bet <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;be&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;Belgium&quot;</span>,<span class="at">output=</span><span class="st">&quot;belgium.html&quot;</span>)</span>
<span id="cb14-39"><a href="#cb14-39" aria-hidden="true" tabindex="-1"></a>dkt <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;dk&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;Denmark&quot;</span>,<span class="at">output=</span><span class="st">&quot;denmark.html&quot;</span>)</span>
<span id="cb14-40"><a href="#cb14-40" aria-hidden="true" tabindex="-1"></a>det <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;de&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;Germany&quot;</span>,<span class="at">output=</span><span class="st">&quot;germany.html&quot;</span>)</span>
<span id="cb14-41"><a href="#cb14-41" aria-hidden="true" tabindex="-1"></a>frt <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;fr&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;France&quot;</span>,<span class="at">output=</span><span class="st">&quot;france.html&quot;</span>)</span>
<span id="cb14-42"><a href="#cb14-42" aria-hidden="true" tabindex="-1"></a>itt <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;it&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;Italy&quot;</span>,<span class="at">output=</span><span class="st">&quot;italy.html&quot;</span>)</span>
<span id="cb14-43"><a href="#cb14-43" aria-hidden="true" tabindex="-1"></a>nlt <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;nl&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;Netherlands&quot;</span>,<span class="at">output=</span><span class="st">&quot;netherlands.html&quot;</span>)</span>
<span id="cb14-44"><a href="#cb14-44" aria-hidden="true" tabindex="-1"></a>ukt <span class="ot">=</span> <span class="fu">runmods</span>(dc2[dc2<span class="sc">$</span>cn<span class="sc">==</span><span class="st">&quot;uk&quot;</span>,],<span class="at">countryn=</span><span class="st">&quot;United Kingdom&quot;</span>,<span class="at">output=</span><span class="st">&quot;uk.html&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="figure-3-models-with-mips" class="level2">
<h2 class="anchored" data-anchor-id="figure-3-models-with-mips">Figure 3: models with MIPs</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>cns <span class="ot">=</span> <span class="fu">unique</span>(deb<span class="sc">$</span>cn) </span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>cns <span class="ot">=</span> cns[<span class="sc">!</span><span class="fu">is.na</span>(cns)]</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>deb<span class="sc">$</span>year_party <span class="ot">=</span> <span class="fu">paste0</span>(de<span class="sc">$</span>year,<span class="st">&quot;_&quot;</span>,deb<span class="sc">$</span>party)</span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>out <span class="ot">=</span> <span class="fu">lapply</span>(cns,<span class="cf">function</span>(x){</span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>  fit <span class="ot">=</span>  <span class="fu">lm</span>(party_share <span class="sc">~</span> <span class="fu">scale</span>(party_share_last)<span class="sc">+</span> <span class="fu">scale</span>(attention_others) <span class="sc">+</span> </span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>                <span class="fu">scale</span>(mip_share),</span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>              <span class="co">#+ scale(share) + factor(topic) </span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>              <span class="at">data=</span> deb <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x))</span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>  test <span class="ot">=</span> <span class="fu">coeftest</span>(fit,<span class="at">vcov =</span> vcovCL, <span class="at">cluster =</span> <span class="sc">~</span>year_party)</span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>  tcofs <span class="ot">=</span> <span class="fu">data.frame</span>(test[<span class="fu">c</span>(<span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>),<span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>)])</span>
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>  <span class="co">#cofs= data.frame(summary(fit)$coeftable)[,c(1,2,3)]</span></span>
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>  cofs<span class="ot">=</span><span class="fu">data.frame</span>(<span class="at">vars=</span><span class="fu">rownames</span>(tcofs),tcofs,<span class="at">country=</span>x)</span>
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>  cofs</span>
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>})</span>
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a>cns <span class="ot">=</span> <span class="fu">unique</span>(dc<span class="sc">$</span>cn)</span>
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a>cns <span class="ot">=</span> cns[cns<span class="sc">!=</span><span class="st">&quot;sw&quot;</span>]</span>
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a>dc<span class="sc">$</span>year_party <span class="ot">=</span> <span class="fu">paste0</span>(dc<span class="sc">$</span>year,<span class="st">&quot;_&quot;</span>,dc<span class="sc">$</span>party)</span>
<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a>out4 <span class="ot">=</span> <span class="fu">lapply</span>(cns,<span class="cf">function</span>(x){</span>
<span id="cb15-22"><a href="#cb15-22" aria-hidden="true" tabindex="-1"></a>  fit <span class="ot">=</span>  <span class="fu">lm</span>(party_share <span class="sc">~</span> <span class="fu">scale</span>(attention_others) <span class="sc">+</span> <span class="fu">scale</span>(party_share_last),</span>
<span id="cb15-23"><a href="#cb15-23" aria-hidden="true" tabindex="-1"></a>              <span class="co">#+ scale(share) + factor(topic) </span></span>
<span id="cb15-24"><a href="#cb15-24" aria-hidden="true" tabindex="-1"></a>              <span class="at">data=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x))</span>
<span id="cb15-25"><a href="#cb15-25" aria-hidden="true" tabindex="-1"></a>  test <span class="ot">=</span> <span class="fu">coeftest</span>(fit,<span class="at">vcov =</span> vcovCL, <span class="at">cluster =</span> <span class="sc">~</span>year_party)</span>
<span id="cb15-26"><a href="#cb15-26" aria-hidden="true" tabindex="-1"></a>  tcofs <span class="ot">=</span> <span class="fu">data.frame</span>(test[<span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>)])</span>
<span id="cb15-27"><a href="#cb15-27" aria-hidden="true" tabindex="-1"></a>  <span class="co">#cofs= data.frame(summary(fit)$coeftable)[,c(1,2,3)]</span></span>
<span id="cb15-28"><a href="#cb15-28" aria-hidden="true" tabindex="-1"></a>  cofs<span class="ot">=</span><span class="fu">data.frame</span>(<span class="at">vars=</span><span class="fu">rownames</span>(tcofs),tcofs,<span class="at">country=</span>x)</span>
<span id="cb15-29"><a href="#cb15-29" aria-hidden="true" tabindex="-1"></a>  cofs</span>
<span id="cb15-30"><a href="#cb15-30" aria-hidden="true" tabindex="-1"></a>})</span>
<span id="cb15-31"><a href="#cb15-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-32"><a href="#cb15-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-33"><a href="#cb15-33" aria-hidden="true" tabindex="-1"></a>test <span class="ot">=</span> <span class="fu">do.call</span>(rbind,out)</span>
<span id="cb15-34"><a href="#cb15-34" aria-hidden="true" tabindex="-1"></a>test <span class="ot">=</span> test <span class="sc">%&gt;%</span> <span class="fu">filter</span>(vars <span class="sc">!=</span> <span class="st">&quot;(Intercept)&quot;</span>)</span>
<span id="cb15-35"><a href="#cb15-35" aria-hidden="true" tabindex="-1"></a>test<span class="sc">$</span>var2 <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Stability&quot;</span>,<span class="st">&quot;Overlap&quot;</span>,<span class="st">&quot;MIP&quot;</span>)</span>
<span id="cb15-36"><a href="#cb15-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-37"><a href="#cb15-37" aria-hidden="true" tabindex="-1"></a>test2 <span class="ot">=</span> <span class="fu">do.call</span>(rbind,out4)</span>
<span id="cb15-38"><a href="#cb15-38" aria-hidden="true" tabindex="-1"></a>test2 <span class="ot">=</span> test2 <span class="sc">%&gt;%</span> <span class="fu">filter</span>(vars <span class="sc">!=</span> <span class="st">&quot;(Intercept)&quot;</span>)</span>
<span id="cb15-39"><a href="#cb15-39" aria-hidden="true" tabindex="-1"></a>test2<span class="sc">$</span>var2 <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Overlap&quot;</span>,<span class="st">&quot;Stability&quot;</span>)</span>
<span id="cb15-40"><a href="#cb15-40" aria-hidden="true" tabindex="-1"></a>test2 <span class="ot">=</span> test2 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(country) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(var2))</span>
<span id="cb15-41"><a href="#cb15-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-42"><a href="#cb15-42" aria-hidden="true" tabindex="-1"></a><span class="co">#png(&quot;country_mip.png&quot;)</span></span>
<span id="cb15-43"><a href="#cb15-43" aria-hidden="true" tabindex="-1"></a>test <span class="sc">%&gt;%</span> <span class="fu">filter</span>(country <span class="sc">!=</span> <span class="st">&quot;SE&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb15-44"><a href="#cb15-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(country,Estimate,<span class="at">color=</span>var2)) <span class="sc">+</span> </span>
<span id="cb15-45"><a href="#cb15-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Estimate<span class="sc">-</span>Std..Error,<span class="at">ymax=</span>Estimate<span class="sc">+</span>Std..Error),</span>
<span id="cb15-46"><a href="#cb15-46" aria-hidden="true" tabindex="-1"></a>                  <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width=</span>.<span class="dv">5</span>)) <span class="sc">+</span></span>
<span id="cb15-47"><a href="#cb15-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span><span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb15-48"><a href="#cb15-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;coefficients&quot;</span>) <span class="sc">+</span> <span class="fu">theme_minimal</span>() <span class="sc">+</span> </span>
<span id="cb15-49"><a href="#cb15-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;gray68&quot;</span>,<span class="st">&quot;black&quot;</span>,<span class="st">&quot;gray38&quot;</span>)) <span class="sc">+</span></span>
<span id="cb15-50"><a href="#cb15-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>(),legend.position </span>
<span id="cb15-51"><a href="#cb15-51" aria-hidden="true" tabindex="-1"></a>        <span class="ot">=</span> <span class="st">&quot;bottom&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co">#dev.off()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="figure-4-comparing-different-drivers-of-overlap" class="level2">
<h2 class="anchored" data-anchor-id="figure-4-comparing-different-drivers-of-overlap">Figure 4: comparing different drivers of overlap</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>allregsF <span class="ot">=</span> <span class="cf">function</span>(x){</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>  dn <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>  fit_oth <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others    , <span class="at">data =</span> dn )</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>  fit_pm <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_pm , <span class="at">data =</span> dn )</span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>  fit_cab <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> cabinet_mean   , <span class="at">data =</span> dn )</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>  fit_big <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_big  , <span class="at">data =</span> dn )</span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a>  fit_near <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_near  , <span class="at">data =</span> dn )</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">bind_rows</span>(<span class="fu">summary</span>(fit_oth)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">summary</span>(fit_pm)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">summary</span>(fit_cab)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">summary</span>(fit_big)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">summary</span>(fit_near)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)]))</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a>  out<span class="sc">$</span>var <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Systemic&quot;</span>,<span class="st">&quot;PM party&quot;</span>,<span class="st">&quot;Nearest party&quot;</span>,<span class="st">&quot;Cabinet mean&quot;</span>,<span class="st">&quot;Biggest party&quot;</span>)</span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>  out<span class="sc">$</span>cn <span class="ot">=</span> x</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>  out</span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a>allregsF2 <span class="ot">=</span> <span class="cf">function</span>(x){</span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>  dn <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a>  dn2 <span class="ot">=</span> dc_early <span class="sc">|&gt;</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)</span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>  fit_oth <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others    , <span class="at">data =</span> dn )</span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>  fit_pm <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_pm , <span class="at">data =</span> dn )</span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a>  fit_cab <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> cabinet_mean   , <span class="at">data =</span> dn )</span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a>  fit_big <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_big  , <span class="at">data =</span> dn )</span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a>  fit_near <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_near  , <span class="at">data =</span> dn )</span>
<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a>  fit_first <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_early,<span class="at">data =</span>dn2)</span>
<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">bind_rows</span>(<span class="fu">summary</span>(fit_oth)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a>                             <span class="fu">summary</span>(fit_pm)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a>                             <span class="fu">summary</span>(fit_cab)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a>                             <span class="fu">summary</span>(fit_big)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a>                             <span class="fu">summary</span>(fit_near)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)],</span>
<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a>                             <span class="fu">summary</span>(fit_first)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)]))</span>
<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a>  out<span class="sc">$</span>var <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Systemic&quot;</span>,<span class="st">&quot;PM party&quot;</span>,<span class="st">&quot;Nearest party&quot;</span>,<span class="st">&quot;Cabinet mean&quot;</span>,<span class="st">&quot;Biggest party&quot;</span>,<span class="st">&quot;First manifesto&quot;</span>)</span>
<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a>  out<span class="sc">$</span>cn <span class="ot">=</span> x</span>
<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a>  out</span>
<span id="cb17-36"><a href="#cb17-36" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb17-37"><a href="#cb17-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-38"><a href="#cb17-38" aria-hidden="true" tabindex="-1"></a>fbe <span class="ot">=</span> <span class="fu">allregsF</span>(<span class="st">&quot;be&quot;</span>)</span>
<span id="cb17-39"><a href="#cb17-39" aria-hidden="true" tabindex="-1"></a>fdk <span class="ot">=</span> <span class="fu">allregsF</span>(<span class="st">&quot;dk&quot;</span>)</span>
<span id="cb17-40"><a href="#cb17-40" aria-hidden="true" tabindex="-1"></a>fde <span class="ot">=</span> <span class="fu">allregsF2</span>(<span class="st">&quot;de&quot;</span>)</span>
<span id="cb17-41"><a href="#cb17-41" aria-hidden="true" tabindex="-1"></a>ffr <span class="ot">=</span> <span class="fu">allregsF2</span>(<span class="st">&quot;fr&quot;</span>)</span>
<span id="cb17-42"><a href="#cb17-42" aria-hidden="true" tabindex="-1"></a>fit <span class="ot">=</span> <span class="fu">allregsF</span>(<span class="st">&quot;it&quot;</span>)</span>
<span id="cb17-43"><a href="#cb17-43" aria-hidden="true" tabindex="-1"></a>fnl <span class="ot">=</span> <span class="fu">allregsF2</span>(<span class="st">&quot;nl&quot;</span>)</span>
<span id="cb17-44"><a href="#cb17-44" aria-hidden="true" tabindex="-1"></a>fuk <span class="ot">=</span> <span class="fu">allregsF2</span>(<span class="st">&quot;uk&quot;</span>)</span>
<span id="cb17-45"><a href="#cb17-45" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-46"><a href="#cb17-46" aria-hidden="true" tabindex="-1"></a>out <span class="ot">=</span> <span class="fu">bind_rows</span>(fbe,fdk,fde,ffr,fit,fnl,fuk)</span>
<span id="cb17-47"><a href="#cb17-47" aria-hidden="true" tabindex="-1"></a>out<span class="sc">$</span>conf.low <span class="ot">=</span> out<span class="sc">$</span>Estimate<span class="sc">-</span>out<span class="sc">$</span>Std..Error</span>
<span id="cb17-48"><a href="#cb17-48" aria-hidden="true" tabindex="-1"></a>out<span class="sc">$</span>conf.high <span class="ot">=</span> out<span class="sc">$</span>Estimate<span class="sc">+</span>out<span class="sc">$</span>Std..Error</span>
<span id="cb17-49"><a href="#cb17-49" aria-hidden="true" tabindex="-1"></a>out<span class="sc">$</span>country <span class="ot">=</span> <span class="fu">factor</span>(out<span class="sc">$</span>cn)</span>
<span id="cb17-50"><a href="#cb17-50" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(out<span class="sc">$</span>country) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;Italy&quot;</span>,<span class="st">&quot;Netherlands&quot;</span>,<span class="st">&quot;United Kingdom&quot;</span>)</span>
<span id="cb17-51"><a href="#cb17-51" aria-hidden="true" tabindex="-1"></a><span class="co">#out$countr = as.character(out$country)</span></span>
<span id="cb17-52"><a href="#cb17-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-53"><a href="#cb17-53" aria-hidden="true" tabindex="-1"></a><span class="co">#png(&quot;art/coeffs.png&quot;,height = 600, width = 600)</span></span>
<span id="cb17-54"><a href="#cb17-54" aria-hidden="true" tabindex="-1"></a>out <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn<span class="sc">!=</span><span class="st">&quot;sw&quot;</span>,<span class="sc">!</span>(cn<span class="sc">==</span><span class="st">&quot;uk&quot;</span> <span class="sc">&amp;</span> var <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;Biggest party&quot;</span>,<span class="st">&quot;Cabinet mean&quot;</span>))) <span class="sc">%&gt;%</span> </span>
<span id="cb17-55"><a href="#cb17-55" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> <span class="co">#geom_point() +</span></span>
<span id="cb17-56"><a href="#cb17-56" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(country,Estimate, <span class="at">ymin=</span>conf.low,<span class="at">ymax =</span>conf.high,<span class="at">color=</span>var),</span>
<span id="cb17-57"><a href="#cb17-57" aria-hidden="true" tabindex="-1"></a>                  <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width=</span>.<span class="dv">3</span>)) <span class="sc">+</span></span>
<span id="cb17-58"><a href="#cb17-58" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span> <span class="co">#scale_color_grey() + </span></span>
<span id="cb17-59"><a href="#cb17-59" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb17-60"><a href="#cb17-60" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>,<span class="at">linetype=</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">color=</span><span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb17-61"><a href="#cb17-61" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="fu">guide_legend</span>(<span class="at">reverse=</span><span class="cn">TRUE</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<figure class="figure">
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co">#dev.off()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="appendix-a12-distribution-of-manifesto-publication-dates" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a12-distribution-of-manifesto-publication-dates">Appendix A12: Distribution of manifesto publication dates</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>ms2 <span class="ot">=</span> ms  <span class="sc">|&gt;</span> <span class="co">#filter(Countries != &quot;Netherlands&quot;) |&gt;</span></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Countries, Elections, <span class="st">`</span><span class="at">Election date</span><span class="st">`</span>,<span class="st">`</span><span class="at">Party name</span><span class="st">`</span>,<span class="at">manif_date =</span> <span class="st">`</span><span class="at">Date of manifesto publication</span><span class="st">`</span>)</span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span>party <span class="ot">=</span> <span class="fu">factor</span>(ms2<span class="sc">$</span><span class="st">`</span><span class="at">Party name</span><span class="st">`</span>)</span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="co">#levels(ms2$party) = c(&quot;U55+&quot;,&quot;AFD&quot;,&quot;Gruenen&quot;,&quot;CDA&quot;,&quot;CDU&quot;,&quot;CU&quot;,&quot;con&quot;,</span></span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a><span class="co">#                     &quot;D66&quot;,NA,NA,&quot;GL&quot;,&quot;lab&quot;,&quot;PvdA&quot;,&quot;lib&quot;,&quot;FDP&quot;,</span></span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a><span class="co">#                    &quot;PC&quot;,&quot;PDS&quot;,NA,&quot;PS&quot;,&quot;PvdD&quot;,&quot;PVV&quot;,&quot;RPR&quot;,&quot;RPR_UDF&quot;,</span></span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a><span class="co">#                   &quot;SGP&quot;,&quot;SPD&quot;,&quot;SP&quot;,&quot;Die Linke&quot;,</span></span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co">#                  NA,&quot;UDF&quot;,&quot;ukip&quot;,&quot;Verts&quot;,&quot;VVD&quot;)</span></span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span>party <span class="ot">=</span> <span class="fu">as.character</span>(ms2<span class="sc">$</span>party)</span>
<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span><span class="st">`</span><span class="at">Party name</span><span class="st">`</span> <span class="ot">=</span> <span class="cn">NULL</span></span>
<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span>cn <span class="ot">=</span> <span class="fu">factor</span>(ms2<span class="sc">$</span>Countries)</span>
<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(ms2<span class="sc">$</span>cn) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;FR&quot;</span>,<span class="st">&quot;DE&quot;</span>,<span class="st">&quot;NL&quot;</span>,<span class="st">&quot;UK&quot;</span>)</span>
<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span>cn <span class="ot">=</span> <span class="fu">as.character</span>(ms2<span class="sc">$</span>cn)</span>
<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>ms2<span class="sc">$</span>Countries <span class="ot">=</span> <span class="cn">NULL</span></span>
<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a>ms3 <span class="ot">=</span> ms2 <span class="sc">|&gt;</span> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(manif_date))</span>
<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>ms4 <span class="ot">=</span> ms3 <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">date2 =</span> (<span class="fu">as.Date</span>(manif_date)),</span>
<span id="cb19-19"><a href="#cb19-19" aria-hidden="true" tabindex="-1"></a>                    <span class="at">elec_date =</span> <span class="fu">as.Date</span>(<span class="st">`</span><span class="at">Election date</span><span class="st">`</span>),</span>
<span id="cb19-20"><a href="#cb19-20" aria-hidden="true" tabindex="-1"></a>                    <span class="at">dist =</span> elec_date <span class="sc">-</span> date2, <span class="at">na.rm=</span>T) </span>
<span id="cb19-21"><a href="#cb19-21" aria-hidden="true" tabindex="-1"></a>ms4 <span class="sc">|&gt;</span></span>
<span id="cb19-22"><a href="#cb19-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="fu">factor</span>(Elections),dist)) <span class="sc">+</span></span>
<span id="cb19-23"><a href="#cb19-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>() <span class="sc">+</span></span>
<span id="cb19-24"><a href="#cb19-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span> <span class="fu">facet_wrap</span>(. <span class="sc">~</span> cn, <span class="at">scales =</span> <span class="st">&quot;free&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-25"><a href="#cb19-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span> <span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb19-26"><a href="#cb19-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_reverse</span>() <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" src="data:image/png;base64,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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a13-models-underlying-figure-4" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a13-models-underlying-figure-4">Appendix A13: Models underlying figure 4</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>allregs <span class="ot">=</span> <span class="cf">function</span>(x){</span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>  dn <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>  fit_oth <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others    , <span class="at">data =</span> dn )</span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>  fit_pm <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_pm , <span class="at">data =</span> dn )</span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>  fit_cab <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> cabinet_mean   , <span class="at">data =</span> dn )</span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>  fit_big <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_big  , <span class="at">data =</span> dn )</span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a>  fit_near <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_near  , <span class="at">data =</span> dn )</span>
<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a>  <span class="co">#fit_first = lm(party_share ~ party_hare_last + party_share_early)</span></span>
<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a>  res <span class="ot">=</span> <span class="fu">list</span>(fit_oth,fit_pm,fit_near,fit_cab,fit_big)</span>
<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a>  res</span>
<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-13"><a href="#cb20-13" aria-hidden="true" tabindex="-1"></a>allregs2 <span class="ot">=</span> <span class="cf">function</span>(x){</span>
<span id="cb20-14"><a href="#cb20-14" aria-hidden="true" tabindex="-1"></a>  dn <span class="ot">=</span> dc <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb20-15"><a href="#cb20-15" aria-hidden="true" tabindex="-1"></a>  dn2 <span class="ot">=</span> dc_early <span class="sc">%&gt;%</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)  </span>
<span id="cb20-16"><a href="#cb20-16" aria-hidden="true" tabindex="-1"></a>  fit_oth <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others    , <span class="at">data =</span> dn )</span>
<span id="cb20-17"><a href="#cb20-17" aria-hidden="true" tabindex="-1"></a>  fit_pm <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_pm , <span class="at">data =</span> dn )</span>
<span id="cb20-18"><a href="#cb20-18" aria-hidden="true" tabindex="-1"></a>  fit_cab <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> cabinet_mean   , <span class="at">data =</span> dn )</span>
<span id="cb20-19"><a href="#cb20-19" aria-hidden="true" tabindex="-1"></a>  fit_big <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_big  , <span class="at">data =</span> dn )</span>
<span id="cb20-20"><a href="#cb20-20" aria-hidden="true" tabindex="-1"></a>  fit_near <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_near  , <span class="at">data =</span> dn )</span>
<span id="cb20-21"><a href="#cb20-21" aria-hidden="true" tabindex="-1"></a>  fit_first <span class="ot">=</span> <span class="fu">lm</span>(party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> party_share_early,<span class="at">data =</span>dn2)</span>
<span id="cb20-22"><a href="#cb20-22" aria-hidden="true" tabindex="-1"></a>  res <span class="ot">=</span> <span class="fu">list</span>(fit_oth,fit_pm,fit_near,fit_cab,fit_big,fit_first)</span>
<span id="cb20-23"><a href="#cb20-23" aria-hidden="true" tabindex="-1"></a>  res</span>
<span id="cb20-24"><a href="#cb20-24" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb20-25"><a href="#cb20-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-26"><a href="#cb20-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-27"><a href="#cb20-27" aria-hidden="true" tabindex="-1"></a>regview1 <span class="ot">=</span> <span class="cf">function</span>(res){</span>
<span id="cb20-28"><a href="#cb20-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">names</span>(res) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Systemic&quot;</span>,<span class="st">&quot;PM party&quot;</span>,<span class="st">&quot;Nearest party&quot;</span>,<span class="st">&quot;Cabinet mean&quot;</span>,<span class="st">&quot;Biggest party&quot;</span>)<span class="co">#,&quot;First manifesto&quot;)</span></span>
<span id="cb20-29"><a href="#cb20-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modelsummary</span>(res,</span>
<span id="cb20-30"><a href="#cb20-30" aria-hidden="true" tabindex="-1"></a>               <span class="at">estimate =</span> <span class="st">&quot;{estimate} ({std.error}){stars}&quot;</span>,</span>
<span id="cb20-31"><a href="#cb20-31" aria-hidden="true" tabindex="-1"></a>               <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;(Intercept)&quot;</span><span class="ot">=</span><span class="st">&quot;Constant&quot;</span>,</span>
<span id="cb20-32"><a href="#cb20-32" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_last&quot;</span><span class="ot">=</span><span class="st">&quot;Party share last&quot;</span>,</span>
<span id="cb20-33"><a href="#cb20-33" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;attention_others&quot;</span><span class="ot">=</span><span class="st">&quot;Attention other parties&quot;</span>,</span>
<span id="cb20-34"><a href="#cb20-34" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_pm&quot;</span><span class="ot">=</span><span class="st">&quot;PM party&quot;</span>,</span>
<span id="cb20-35"><a href="#cb20-35" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_near&quot;</span> <span class="ot">=</span> <span class="st">&quot;Nearest party&quot;</span>,</span>
<span id="cb20-36"><a href="#cb20-36" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;cabinet_mean&quot;</span> <span class="ot">=</span> <span class="st">&quot;Cabinet mean&quot;</span>,</span>
<span id="cb20-37"><a href="#cb20-37" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_big&quot;</span><span class="ot">=</span><span class="st">&quot;Biggest party&quot;</span>),<span class="co">#,</span></span>
<span id="cb20-38"><a href="#cb20-38" aria-hidden="true" tabindex="-1"></a>               <span class="co"># &quot;party_share_early&quot; = &quot;First manifesto&quot;),</span></span>
<span id="cb20-39"><a href="#cb20-39" aria-hidden="true" tabindex="-1"></a>               <span class="at">statistic =</span> <span class="cn">NULL</span>,<span class="co">#stars=T,</span></span>
<span id="cb20-40"><a href="#cb20-40" aria-hidden="true" tabindex="-1"></a>               <span class="at">vcov =</span> vcovCL, <span class="at">cluster =</span> <span class="sc">~</span>year,</span>
<span id="cb20-41"><a href="#cb20-41" aria-hidden="true" tabindex="-1"></a>               <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|Std.|FE|AIC|BIC|Within|Pseudo|Log&#39;</span>)</span>
<span id="cb20-42"><a href="#cb20-42" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb20-43"><a href="#cb20-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-44"><a href="#cb20-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-45"><a href="#cb20-45" aria-hidden="true" tabindex="-1"></a>regview2 <span class="ot">=</span> <span class="cf">function</span>(res){</span>
<span id="cb20-46"><a href="#cb20-46" aria-hidden="true" tabindex="-1"></a>  <span class="fu">names</span>(res) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Systemic&quot;</span>,<span class="st">&quot;PM party&quot;</span>,<span class="st">&quot;Nearest party&quot;</span>,<span class="st">&quot;Cabinet mean&quot;</span>,<span class="st">&quot;Biggest party&quot;</span>,<span class="st">&quot;First manifesto&quot;</span>)</span>
<span id="cb20-47"><a href="#cb20-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modelsummary</span>(res,</span>
<span id="cb20-48"><a href="#cb20-48" aria-hidden="true" tabindex="-1"></a>               <span class="at">estimate =</span> <span class="st">&quot;{estimate} ({std.error}){stars}&quot;</span>,</span>
<span id="cb20-49"><a href="#cb20-49" aria-hidden="true" tabindex="-1"></a>               <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;(Intercept)&quot;</span><span class="ot">=</span><span class="st">&quot;Constant&quot;</span>,</span>
<span id="cb20-50"><a href="#cb20-50" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_last&quot;</span><span class="ot">=</span><span class="st">&quot;Party share last&quot;</span>,</span>
<span id="cb20-51"><a href="#cb20-51" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;attention_others&quot;</span><span class="ot">=</span><span class="st">&quot;Attention other parties&quot;</span>,</span>
<span id="cb20-52"><a href="#cb20-52" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_pm&quot;</span><span class="ot">=</span><span class="st">&quot;PM party&quot;</span>,</span>
<span id="cb20-53"><a href="#cb20-53" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_near&quot;</span> <span class="ot">=</span> <span class="st">&quot;Nearest party&quot;</span>,</span>
<span id="cb20-54"><a href="#cb20-54" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;cabinet_mean&quot;</span> <span class="ot">=</span> <span class="st">&quot;Cabinet mean&quot;</span>,</span>
<span id="cb20-55"><a href="#cb20-55" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_big&quot;</span><span class="ot">=</span><span class="st">&quot;Biggest party&quot;</span>,</span>
<span id="cb20-56"><a href="#cb20-56" aria-hidden="true" tabindex="-1"></a>                               <span class="st">&quot;party_share_early&quot;</span> <span class="ot">=</span> <span class="st">&quot;First manifesto&quot;</span>),</span>
<span id="cb20-57"><a href="#cb20-57" aria-hidden="true" tabindex="-1"></a>               <span class="at">statistic =</span> <span class="cn">NULL</span>,<span class="co">#stars=T,</span></span>
<span id="cb20-58"><a href="#cb20-58" aria-hidden="true" tabindex="-1"></a>               <span class="at">vcov =</span> vcovCL, <span class="at">cluster =</span> <span class="sc">~</span>year,</span>
<span id="cb20-59"><a href="#cb20-59" aria-hidden="true" tabindex="-1"></a>               <span class="at">gof_omit =</span> <span class="st">&#39;DF|Deviance|Std.|FE|AIC|BIC|Within|Pseudo|Log&#39;</span>)</span>
<span id="cb20-60"><a href="#cb20-60" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb20-61"><a href="#cb20-61" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-62"><a href="#cb20-62" aria-hidden="true" tabindex="-1"></a>a_be <span class="ot">=</span> <span class="fu">allregs</span>(<span class="st">&quot;be&quot;</span>)</span>
<span id="cb20-63"><a href="#cb20-63" aria-hidden="true" tabindex="-1"></a>a_de <span class="ot">=</span> <span class="fu">allregs2</span>(<span class="st">&quot;de&quot;</span>)</span>
<span id="cb20-64"><a href="#cb20-64" aria-hidden="true" tabindex="-1"></a>a_dk <span class="ot">=</span> <span class="fu">allregs</span>(<span class="st">&quot;dk&quot;</span>)</span>
<span id="cb20-65"><a href="#cb20-65" aria-hidden="true" tabindex="-1"></a>a_fr <span class="ot">=</span> <span class="fu">allregs2</span>(<span class="st">&quot;fr&quot;</span>)</span>
<span id="cb20-66"><a href="#cb20-66" aria-hidden="true" tabindex="-1"></a>a_it <span class="ot">=</span> <span class="fu">allregs</span>(<span class="st">&quot;it&quot;</span>)</span>
<span id="cb20-67"><a href="#cb20-67" aria-hidden="true" tabindex="-1"></a>a_nl <span class="ot">=</span> <span class="fu">allregs2</span>(<span class="st">&quot;nl&quot;</span>)</span>
<span id="cb20-68"><a href="#cb20-68" aria-hidden="true" tabindex="-1"></a>a_uk <span class="ot">=</span> <span class="fu">allregs2</span>(<span class="st">&quot;uk&quot;</span>)</span>
<span id="cb20-69"><a href="#cb20-69" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-70"><a href="#cb20-70" aria-hidden="true" tabindex="-1"></a><span class="fu">regview1</span>(a_be)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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                <th scope="col"> </th>
                <th scope="col">Systemic</th>
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        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.001 (0.001)   </td>
                  <td>0.008 (0.003)** </td>
                  <td>0.010 (0.003)***</td>
                  <td>0.003 (0.002)+  </td>
                  <td>0.009 (0.003)***</td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.279 (0.037)***</td>
                  <td>0.358 (0.062)***</td>
                  <td>0.451 (0.059)***</td>
                  <td>0.203 (0.050)***</td>
                  <td>0.341 (0.045)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.709 (0.049)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.496 (0.085)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.361 (0.047)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.751 (0.068)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.510 (0.061)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>2068            </td>
                  <td>1408            </td>
                  <td>1408            </td>
                  <td>1408            </td>
                  <td>1518            </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.535           </td>
                  <td>0.483           </td>
                  <td>0.456           </td>
                  <td>0.605           </td>
                  <td>0.425           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.534           </td>
                  <td>0.482           </td>
                  <td>0.455           </td>
                  <td>0.604           </td>
                  <td>0.425           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview2</span>(a_de)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
                <th scope="col">First manifesto</th>
              </tr>
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        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.003 (0.002)   </td>
                  <td>0.001 (0.001)   </td>
                  <td>0.007 (0.002)** </td>
                  <td>-0.001 (0.002)  </td>
                  <td>0.003 (0.001)*  </td>
                  <td>0.009 (0.004)*  </td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.349 (0.037)***</td>
                  <td>0.378 (0.049)***</td>
                  <td>0.482 (0.049)***</td>
                  <td>0.285 (0.046)***</td>
                  <td>0.395 (0.048)***</td>
                  <td>0.431 (0.060)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.602 (0.052)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.585 (0.040)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.365 (0.054)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.721 (0.038)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.533 (0.041)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>First manifesto        </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.406 (0.064)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>865             </td>
                  <td>651             </td>
                  <td>651             </td>
                  <td>651             </td>
                  <td>652             </td>
                  <td>420             </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.607           </td>
                  <td>0.704           </td>
                  <td>0.614           </td>
                  <td>0.735           </td>
                  <td>0.704           </td>
                  <td>0.608           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.606           </td>
                  <td>0.704           </td>
                  <td>0.613           </td>
                  <td>0.734           </td>
                  <td>0.703           </td>
                  <td>0.606           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
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        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview1</span>(a_dk)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>-0.004 (0.002)  </td>
                  <td>0.013 (0.002)***</td>
                  <td>0.017 (0.002)***</td>
                  <td>0.011 (0.002)***</td>
                  <td>0.014 (0.002)***</td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.315 (0.048)***</td>
                  <td>0.336 (0.046)***</td>
                  <td>0.420 (0.054)***</td>
                  <td>0.289 (0.048)***</td>
                  <td>0.306 (0.049)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.569 (0.054)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.414 (0.040)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.251 (0.034)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.497 (0.026)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.418 (0.035)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>2816            </td>
                  <td>2560            </td>
                  <td>2560            </td>
                  <td>2560            </td>
                  <td>2560            </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.365           </td>
                  <td>0.418           </td>
                  <td>0.347           </td>
                  <td>0.443           </td>
                  <td>0.419           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.365           </td>
                  <td>0.417           </td>
                  <td>0.346           </td>
                  <td>0.443           </td>
                  <td>0.419           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.07            </td>
                  <td>0.06            </td>
                  <td>0.07            </td>
                  <td>0.06            </td>
                  <td>0.06            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview2</span>(a_fr)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
                <th scope="col">First manifesto</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.007 (0.003)*  </td>
                  <td>0.005 (0.002)*  </td>
                  <td>0.017 (0.004)***</td>
                  <td>0.004 (0.003)   </td>
                  <td>0.007 (0.002)** </td>
                  <td>0.013 (0.003)***</td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.245 (0.062)***</td>
                  <td>0.222 (0.064)***</td>
                  <td>0.374 (0.087)***</td>
                  <td>0.161 (0.046)***</td>
                  <td>0.236 (0.065)***</td>
                  <td>0.303 (0.071)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.627 (0.081)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.670 (0.082)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.253 (0.082)** </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.768 (0.085)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.588 (0.080)***</td>
                  <td>                </td>
                </tr>
                <tr>
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                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.460 (0.047)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>790             </td>
                  <td>460             </td>
                  <td>500             </td>
                  <td>460             </td>
                  <td>500             </td>
                  <td>720             </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.385           </td>
                  <td>0.467           </td>
                  <td>0.311           </td>
                  <td>0.501           </td>
                  <td>0.468           </td>
                  <td>0.357           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.383           </td>
                  <td>0.464           </td>
                  <td>0.309           </td>
                  <td>0.498           </td>
                  <td>0.466           </td>
                  <td>0.355           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.04            </td>
                  <td>0.04            </td>
                  <td>0.04            </td>
                  <td>0.04            </td>
                  <td>0.04            </td>
                  <td>0.04            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview1</span>(a_it)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.002 (0.002)   </td>
                  <td>0.006 (0.007)   </td>
                  <td>0.018 (0.004)***</td>
                  <td>0.000 (0.006)   </td>
                  <td>0.002 (0.005)   </td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.144 (0.055)** </td>
                  <td>0.476 (0.065)***</td>
                  <td>0.581 (0.054)***</td>
                  <td>0.335 (0.041)***</td>
                  <td>0.411 (0.058)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.787 (0.049)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.470 (0.037)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.146 (0.012)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.702 (0.117)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.624 (0.090)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>880             </td>
                  <td>320             </td>
                  <td>400             </td>
                  <td>320             </td>
                  <td>420             </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.469           </td>
                  <td>0.465           </td>
                  <td>0.374           </td>
                  <td>0.517           </td>
                  <td>0.473           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.467           </td>
                  <td>0.462           </td>
                  <td>0.371           </td>
                  <td>0.514           </td>
                  <td>0.471           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.05            </td>
                  <td>0.06            </td>
                  <td>0.06            </td>
                  <td>0.06            </td>
                  <td>0.06            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview2</span>(a_nl)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
                <th scope="col">First manifesto</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.004 (0.001)***</td>
                  <td>0.013 (0.004)***</td>
                  <td>0.013 (0.003)***</td>
                  <td>0.009 (0.003)** </td>
                  <td>0.014 (0.003)***</td>
                  <td>0.007 (0.001)***</td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.301 (0.092)** </td>
                  <td>0.441 (0.116)***</td>
                  <td>0.484 (0.125)***</td>
                  <td>0.385 (0.118)** </td>
                  <td>0.440 (0.116)***</td>
                  <td>0.684 (0.068)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.641 (0.083)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.304 (0.082)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.257 (0.079)** </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.438 (0.102)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.297 (0.079)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>First manifesto        </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.204 (0.061)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>1672            </td>
                  <td>1235            </td>
                  <td>1235            </td>
                  <td>1235            </td>
                  <td>1235            </td>
                  <td>399             </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.405           </td>
                  <td>0.482           </td>
                  <td>0.412           </td>
                  <td>0.522           </td>
                  <td>0.477           </td>
                  <td>0.681           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.405           </td>
                  <td>0.481           </td>
                  <td>0.411           </td>
                  <td>0.521           </td>
                  <td>0.477           </td>
                  <td>0.680           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.03            </td>
                  <td>0.02            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">regview2</span>(a_uk)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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    <div class="container">
      <table class="table table-borderless" id="tinytable_rrpq0wh0d3o40xuh64ew" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Systemic</th>
                <th scope="col">PM party</th>
                <th scope="col">Nearest party</th>
                <th scope="col">Cabinet mean</th>
                <th scope="col">Biggest party</th>
                <th scope="col">First manifesto</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Constant               </td>
                  <td>0.007 (0.002)***</td>
                  <td>0.004 (0.001)***</td>
                  <td>0.008 (0.002)***</td>
                  <td>0.004 (0.001)***</td>
                  <td>0.004 (0.001)***</td>
                  <td>0.006 (0.002)** </td>
                </tr>
                <tr>
                  <td>Party share last       </td>
                  <td>0.380 (0.087)***</td>
                  <td>0.340 (0.065)***</td>
                  <td>0.515 (0.058)***</td>
                  <td>0.344 (0.063)***</td>
                  <td>0.340 (0.065)***</td>
                  <td>0.340 (0.068)***</td>
                </tr>
                <tr>
                  <td>Attention other parties</td>
                  <td>0.486 (0.074)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>PM party               </td>
                  <td>                </td>
                  <td>0.555 (0.043)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Nearest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.318 (0.037)***</td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Cabinet mean           </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.566 (0.045)***</td>
                  <td>                </td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>Biggest party          </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.555 (0.043)***</td>
                  <td>                </td>
                </tr>
                <tr>
                  <td>First manifesto        </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>                </td>
                  <td>0.553 (0.039)***</td>
                </tr>
                <tr>
                  <td>Num.Obs.               </td>
                  <td>462             </td>
                  <td>441             </td>
                  <td>441             </td>
                  <td>441             </td>
                  <td>441             </td>
                  <td>462             </td>
                </tr>
                <tr>
                  <td>R2                     </td>
                  <td>0.565           </td>
                  <td>0.660           </td>
                  <td>0.572           </td>
                  <td>0.666           </td>
                  <td>0.660           </td>
                  <td>0.663           </td>
                </tr>
                <tr>
                  <td>R2 Adj.                </td>
                  <td>0.563           </td>
                  <td>0.658           </td>
                  <td>0.570           </td>
                  <td>0.664           </td>
                  <td>0.658           </td>
                  <td>0.662           </td>
                </tr>
                <tr>
                  <td>RMSE                   </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                  <td>0.02            </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</section>
<section id="figure-5-articles-mentioning-wealth-tax-and-single-german-parties-in-die-welt" class="level2">
<h2 class="anchored" data-anchor-id="figure-5-articles-mentioning-wealth-tax-and-single-german-parties-in-die-welt">Figure 5: Articles mentioning “wealth tax” and single German parties in Die Welt</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>mw <span class="ot">=</span> mw <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">yearmonth =</span> <span class="fu">as.Date</span>(yearmonth)) <span class="sc">|&gt;</span></span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(yearmonth <span class="sc">&gt;</span> <span class="fu">as.Date</span>(<span class="st">&quot;2017-12-01&quot;</span>))</span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>mw2 <span class="ot">=</span> mw <span class="sc">%&gt;%</span> <span class="fu">filter</span>(outlet <span class="sc">==</span> <span class="st">&quot;welt&quot;</span>) <span class="sc">|&gt;</span> dplyr<span class="sc">::</span><span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">10</span>)) <span class="sc">|&gt;</span></span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">names_to =</span> <span class="st">&quot;series&quot;</span>,<span class="at">values_to =</span> <span class="st">&quot;value&quot;</span>,<span class="sc">-</span><span class="fu">c</span>(yearmonth)) <span class="sc">|&gt;</span></span>
<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">yearmonth =</span> <span class="fu">as.Date</span>(yearmonth))</span>
<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>tot <span class="ot">=</span> <span class="fu">ifelse</span>(mw2<span class="sc">$</span>series <span class="sc">==</span> <span class="st">&quot;no_articles&quot;</span>,<span class="dv">1</span>,<span class="dv">0</span>)</span>
<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>labs <span class="ot">=</span> <span class="cn">NA</span></span>
<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>labs[mw2<span class="sc">$</span>yearmonth <span class="sc">==</span> <span class="fu">as.Date</span>(<span class="st">&quot;2019-08-01&quot;</span>) <span class="sc">&amp;</span> mw2<span class="sc">$</span>tot <span class="sc">==</span> T] <span class="ot">=</span> <span class="st">&quot;SPD leader&#39;s proposal </span><span class="sc">\n</span><span class="st">for Wealth Tax&quot;</span></span>
<span id="cb27-11"><a href="#cb27-11" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>labs[mw2<span class="sc">$</span>yearmonth <span class="sc">==</span> <span class="fu">as.Date</span>(<span class="st">&quot;2019-12-01&quot;</span>) <span class="sc">&amp;</span> mw2<span class="sc">$</span>tot <span class="sc">==</span> T] <span class="ot">=</span> <span class="st">&quot;Proposal accepted </span><span class="sc">\n</span><span class="st">at SPD party conf.&quot;</span></span>
<span id="cb27-12"><a href="#cb27-12" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>labs[mw2<span class="sc">$</span>yearmonth <span class="sc">==</span> <span class="fu">as.Date</span>(<span class="st">&quot;2020-12-01&quot;</span>) <span class="sc">&amp;</span> mw2<span class="sc">$</span>tot <span class="sc">==</span> T] <span class="ot">=</span> <span class="st">&quot;Debate on Wealth </span><span class="sc">\n</span><span class="st">Tax and Pandemic&quot;</span> </span>
<span id="cb27-13"><a href="#cb27-13" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>series[mw2<span class="sc">$</span>series<span class="sc">==</span><span class="st">&quot;no_articles&quot;</span>]<span class="ot">=</span><span class="st">&quot;total&quot;</span></span>
<span id="cb27-14"><a href="#cb27-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-15"><a href="#cb27-15" aria-hidden="true" tabindex="-1"></a>cols <span class="ot">=</span> <span class="fu">c</span>(<span class="at">total=</span><span class="st">&quot;grey&quot;</span>,<span class="at">spd=</span><span class="st">&quot;red&quot;</span>,<span class="st">&quot;cdu&quot;</span><span class="ot">=</span><span class="st">&quot;black&quot;</span>,<span class="st">&quot;fdp&quot;</span><span class="ot">=</span><span class="st">&quot;yellow&quot;</span>,<span class="st">&quot;grüne&quot;</span><span class="ot">=</span><span class="st">&quot;green&quot;</span>,<span class="st">&quot;linke&quot;</span><span class="ot">=</span><span class="st">&quot;darkred&quot;</span>)</span>
<span id="cb27-16"><a href="#cb27-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-17"><a href="#cb27-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-18"><a href="#cb27-18" aria-hidden="true" tabindex="-1"></a>mw2 <span class="sc">|&gt;</span> <span class="fu">filter</span>(yearmonth <span class="sc">&gt;</span> <span class="fu">as.Date</span>(<span class="st">&quot;2017-12-01&quot;</span>)) <span class="sc">|&gt;</span></span>
<span id="cb27-19"><a href="#cb27-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(yearmonth,value,<span class="at">color=</span>series,<span class="at">label=</span>labs)) <span class="sc">+</span> </span>
<span id="cb27-20"><a href="#cb27-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="at">alpha=</span>.<span class="dv">6</span>) <span class="sc">+</span> </span>
<span id="cb27-21"><a href="#cb27-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text_repel</span>(<span class="at">max.overlaps =</span> <span class="cn">Inf</span>,<span class="at">nudge_x =</span> .<span class="dv">15</span>,</span>
<span id="cb27-22"><a href="#cb27-22" aria-hidden="true" tabindex="-1"></a>                  <span class="at">segment.curvature =</span> <span class="sc">-</span><span class="dv">1</span>,</span>
<span id="cb27-23"><a href="#cb27-23" aria-hidden="true" tabindex="-1"></a>                  <span class="at">segment.ncp =</span> <span class="dv">3</span>,<span class="co">#box.padding = .5,</span></span>
<span id="cb27-24"><a href="#cb27-24" aria-hidden="true" tabindex="-1"></a>                  <span class="at">nudge_y =</span> <span class="dv">2</span>,<span class="at">show.legend =</span> F) <span class="sc">+</span> </span>
<span id="cb27-25"><a href="#cb27-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> </span>
<span id="cb27-26"><a href="#cb27-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_linewidth_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)) <span class="sc">+</span></span>
<span id="cb27-27"><a href="#cb27-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> cols) <span class="sc">+</span></span>
<span id="cb27-28"><a href="#cb27-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="st">&quot;serie&quot;</span>), <span class="at">linewidth =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb27-29"><a href="#cb27-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb27-30"><a href="#cb27-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;Number of articles&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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iQe2K69e7/FtfXKwJmiQmV9rRgyejiboe5EhVL3YWau7Z+fae+u8JOfvB5C6H3J4jsAwG0Q4IE56O4NH5UzJspHpUonwg8m4K6pK8l1y9OlLweun7mve5RR8q6zgdzl/N45Pjtudm8gjZvdVwGdOnhRuTTavl9p7+XI36aTcfpobie/wvR39/o/FtvfcUiSZqtRE98BAG6BKfTAHLzs1VjLHEG/YgP3GXaj6yxuH5gan53fswrZdQvQD3e0l9/HvUzo30AaClHI3mP9cuOby3qcj+Z29vFmubvXS5VyUovTkMT1zkfiOwDAfAnwwM1dWdc9hH7q7AXgnmuK1mXpF7LrnGxcfh8tZDdmA7nu+4ekfpKEq2S8geh8noaXL1+GNEmSNJnD1PkQwkwvN27FhHcXdSN8m/gOADBvAjwwP2PT+PwGpQfO1h1YH5/fLxeym/MG8Gmr0YjnFtcXzQx3133cIcztGQMA0CfAA/MzOsA+8Ic56k6NT1+GEF2R3y+N1of0mmw5xXbll7ecizqKhULhZbO9M3p+zXZ3/dJ1IYQkbpYKt73XHQDAq2WhA/x33/zmy3//3UkchxBCKJe3/uVf33/vrTcyGv7ms//Zb7f18zHNgFtyxRr3tmvWyE+tl+DT8Oiq/D40Wl8I41a6j5/hP0Yvqg7tstZ1i+Xl7sRsd9cv5F8sJklyB3vdAQC8aha3Cv03n+1/8MkX3VQeQojjky8++WD/N9+NNPzZB0PtTr745IPPvrmzjgL90uRDO7b19UvHzWtRd/uCadK6cvw9hMGi9Z23CBkNe6Xdx23OljZrJycnJye1dhn93l505dF827/bvJrp7n74X93QXy5VSp297uJGxrYDAADMalED/DeffXISh1De+vjXX7f9+uOfl0MI8RfD4fybzz45CSGUf95p2Gl28slo0gduTVQqt7cHz9oprrvHXCiW5zYe29njPU2b102L7yX4uLPTXdZLhMLVkbO3LfoEN9C72wdp3N29/POfu6G/FPU3DhThAQDmaTED/He/+feTEMLWx08/7E2Ff+Ot955+vBVCCCfVb0YbdufM95rFv/sPCR7uTj8C12qNVnfJe5q2Gt3V1Jm7is+sM2ieTFCWrjNan7kBfO90/chZqzWa/YH4tNWsddZ892+g8/ogpHGjMdA0bTVqJwNrx69f+d+ZudAZ+Z9zpYBZTX93rUbSnljfe8PRe6MjwgMAzM9iroH/4x/iEMLWxluXPn9rYyucnITmH74Lb70RQvjuP36X1fCt939ePvki/t1/fPfee5bCwx2JSuvroVaL05Am9dqlYdqoWK5U5rscurMMvu3KkueDtdHH5Pcw3P+4nsSX/1xeH1jPHZXKxaSeZN1rVCyXo6QeX1USoN+4u/g+rp3EYaoqerdoyrtrNTpF7YY2jitU1opJPQnWwgMAzM1iBvi3Pvz66w8naDcu6L/x01IIcfyHP4YgwMPdiUrrW4VWsxkn/d3HoqhYrpQKcypeN2ggwV+zZVl/E7vx+T2Ewf6n6eANFMul0uXjCpX19eLQrbYbFgpRFEJUjOtJ6O5ed9VNVNbXQqOe9Me1r4n8d2OKu8uO7+2T9CJ8s1VagBcTAAB5t5gBfpxvqichhPK//I92LP/uD80QQvmn/zzS8J9/Wg4h7o3UA3NUqGxtVcb+NSqUKoXS+L/32pXWt0q32ZHZWnb6P0XTzPNmXHBcH6JCZX3StiHc9MFNfvSkd9f+RxzH5dGEPsVzBwDgenkK8N0F7/96w3nxz549G/enOL48bfZ+LVp/eBj8rrgNflfcBr8rboPfFbfB74q7kZ8A/81nH3wRh1D++fuXJ8yPNWYO/TvvvJPZ/NmzZ+VyefYezlscxwvVHx4Gvytug98Vt8Hvitvgd8VtmPx31Wg0brszPGyLWYV+xDef7X9yEkJ56+OnUwy/Z02uBwAAgDzKwQj8d7/Z74y9/3qa9A5wh05OTkIIW1tb990RAAAerEUfgf/msyvS+xs/LYXORPlL2uXpSz+V9wEAAHgYFjnAf/fNZz/75CQO5a2Pv84ee//nn5ZDCM0/fHf5yLHl6QEAACCXFjfAf/PZB5+ctIfePxxXtu6N//Ev5RDiL778ZvjQL7+IB3abAwAAgNxb0AD/3W/2u+n9ymXv7QQfTj7Z/+yb9jD8d9+0D5XfAQAAeEgWs4hdewg9hPiLD372Rcbftz7+ujMq/8Z7//bxHz745CQ++eSDk6EWCt4BAADwgCzkCPw31ZPrG3W98daHv/7451v9fRfLWz//9ddjZ90DAABAHi3kCPxbH3799YdTtH/jrfc+fOu9aY4AAACAfFnIEXgAAABgmAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkwGv33QGAh2Bra+u+uwAAwANnBB4AAAByQIAHmIPT09PT09P77gUAAA+ZAA8wNzI8AAC3R4AHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHuKnT09P77gIAAA+fAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAPOxuroabCkHAMCtEeABAAAgBwR4AAAAyAEBHgAAAHJAgAcAAIAcEOABAAAgBwR4AAAAyAEBHgAAAHJAgAcAAIAcEOABAAAgBwR4gBs5PT0NIayurt53RwAAeOAEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR4AAAByQIAHAACAHBDgAQAAIAcEeAAAAMgBAR5gblZXV0MIp6en990RAAAeIAEeAAAAckCABwAAgBwQ4AEAACAHBHiA2bWXu7eXvgMAwK0S4AEAACAHli4uLu67D4vi2bNnlUrlvnsB5Ml//ud/hhD+6Z/+6YpPAADaGo1GCOGdd965746QV6/ddwcWS7lcvu8u9MVxvFD94WHwu5qvv/3tb2H4Px2jn7wK/K64DX5X3Aa/K27D5L+rdoCHmZlCDwAAADkgwAMAAEAOCPAAAACQAwI8AAAA5IAADwAAADkgwAMAAEAOCPAAAACQAwI8AAAA5IAADwAAADkgwAMAAEAOCPAAAACQAwI8AAAA5IAADwAAADkgwAPM6PT0NISwurp63x0BAOCVIMADAABADgjwAAAAkAMCPMA8tWfUt2fXAwDAHAnwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8wi9PT0xDC6urqfXcEAIBXhQAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPAAAAOSDAAwAAQA4I8AAAAJADAjwAAADkgAAPMGerq6shhNPT0/vuCAAAD4oADwAAADkgwAMAAEAOCPAAAACQAwI8AAAA5IAADwAAADkgwAMAAEAOCPAAAACQAwI8wNTae7y393sHAIC7IcADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA5IMADAABADgjwAAAAkAMCPAAAAOSAAA8AAAA58Np9dwDgGq3GST254u9RFEXFcqlUiO6sS7nTeYZReX295DHdpbRZq8VpCMW1rUrhvjsDAOSdEXgg79I0TeJ67aTWTO+7K+RO2mrU/HIAgJwQ4IGHIo0lMabSapzU6onfDACQF6bQAzkxdvp32mo26nEaQghp3GyVFmGm8urq6unp6enp6erq6n33BQCAB8IIPJB3UaG0vl7uRPskad1vbwAA4JYI8MBDEBWKnQSfprc9I/r09DSEYGgdAIA7Zgo98CBEURTCcHTv1P8urm2VQrMZx521zlFUrgzMxU9bzWacJL3YH0XFzJL2/Tru/xh+/Mv339dOTjqHRMXyFTXwJz1/ZuOrW4c0bbUut59bRf6pTz7c+at7fpPGk3xH66XQGvjOR7+jbnH49j/i2kkcRldpTPXdZdxTubIIyzkAgAdEgAcehN7AexRdjldJo5YMbEOX9lsMpbjeiZK4niTFcqWSveHay++T5PsfB49I4noSZy3R//H75KT2pwnPn7YatZHt8tLOyYtr65eyYNpqNEbrr6Vpmsb1JBltP5WpT/7D//rupPnDaM8zOzL62KdqfNVT6XjZrNWHDhr/HY0z5XUz76leS4rl4mTXAwCYgAAPPARpqze+/ujSn5JkYAP0tNV8+aidvfqRKyquVUqFKAohpGmr2agnaZrEtRBl7NydxvUQwmt/9w//1//5f7eP6GTdNK41hg74sRf0Jzl/2my003tUXqv0xni79fnSpN4oDp48bXYCdlRcq1T6zTu5e6T9dA9zypOnzdp3f/5huPOd1mlSr0VDqXngsU/ReGgEvf8Q67WQlfnjehxCVCxXSqVx31FUWt8qDQ3ZDwb7aa/bagz8lDpPrH3RJL78TgYAYHbWwAP5lqatZqM3+hkVs+Y3F9e6+SwqdNNYq9mNkevrlUJ3UD6KCpVuRbyk3sguiPd3Pyn+5B96RxQq62vtUdYkHtjFrvV9J71PdP7uC4ji2vrgDO2oUOqdfLA6X7fzxbX1ylDzXl9uUAtg2pMPPsle56NCpdK+zTRuZvR8ksZps9HvycBjGXiI6dAzH1BcW6+UrvuOxpj2umkzTrr31H9iA980AMCcGIEHcqK7UPkqxXLWDOlicXQgvZe5MibKR6VKOanFaUjiZqlw+e/Fn/zk8n85C5W1YlJPBnaxS5vxX0II4bUpz5+mabj0CqJQ2dqqDJ+glXQ6XxodY3/UKQaQpi9DmCU+TnvyTvvRZx+VysW4ngzd1DSN+y81sqYSdB9i5s6BGYeMfkfjTHvd3guP0R9f764AAOZCgAceiPbs5aw/jMyqD+Flr9JYZsaNxqfgYvHvwn+NHFAoFkMyED+75399wvP3PohrtaRYLJcKhewj21cbzfQjp57ZlCfvDsY/Gn1LMnqqbuOMVyojjXs5OqPtUGdGX3pkH3L5Oxpj2utedU/9iwIAzIMAD+TctYXXM0J62svvo9k+hDA41Hwp7EXRo/DX0QDfP/HLEPqh7rXXJj1/obJWTNtLzNuV0toXuzbLd+/m5cuXIU2SJE3mvI3edSfvvKp4/b+PudOsxmMfe0bbkNRPrg7AI29Zrjn9NXMTpr3uy6sf+KObvlABAOgT4IGcmKaE+MTnHBeNx45jR1EU/prxeXZOe/21yc9fqKyvF4e3Letl+ayd6tJWoxHPO67fyclv27hv9Vaz9EQvJQAAbkaAB15hY2dTjy0Bl6bpP2Z9nj0O+8OPafZgb/b5o0KpUihVOnuwD4x4p0lcT9K1Xun3y5uWRR3FQqHwsnlys1XXt3ry6RXXpi2nP+5bvWas/GbXnbnmAADA5AR44BV0xRr3tmvWyF9x4keD5//xx9nOH0WFqFIohUp/J7nQL3nXq5I+tM1b79RTdXjEjCf/4a9Zd9p9F3ApDGc/9kuNx65iuKlrxsqnve414/rTvTUAALiSbeSAV9GjTnAe2pytr1ssfTTrpUnrx/EH9Hax657/L5OdP23WTk5OTk4y9q0b2EkuTV+G0K+yFpVHA3b/1DOa+uSFYrtzLzPudKQeXLdx1mO/3Lj7biNNWtkRuPvQapc3hhtzxOXvaIxprxsVitG4e7rJbn4AAKMEeOBVFJXK4/cF710wcdwAACAASURBVO4xl7krXRr/1/eXI3yrUb+UDaNS+e+mOH8vBGbuUn5tyb2MU9+G7JP3QvlI33v7q/Xrs0/TuFDq7g3fyHoqV+zelnXE6Hc0zrTX7X159ZH3L73pDAAAcyHAA6+mfkyr1Rqt7jhpmrYa3RXgmTuhhxB+/D5Jvv9L94BWs1GrZ+wpX/iHf3ht8vN3XygMt26fv9u+mxi7gTGNG43+IHGathq1k4HF67ON/c5w8kJlrdf3Zqv3XPqPZfAxTtM4KlUGvqPmQHdazdpJ1tl7Lp1+zHfUnSjRGWzvvSiZ8rq99kl9oPnANw0AMCfWwAOvqKi0vh5qtTgNaVKvXRpajorlSiWr5n1ULP41SX78/s+1k5Ohz0dq5L/2D8ViSJLvf5zs/IXKejkd05v2+XuryKNSuZjUk6ymUbFcjpJ6fNX6/qvNcvJC5Y2f/Pm7P/8Q0rhei4cPWbu8dUD/Rq9vPPgddffWG/xz9sYEUbEYkmTk9BnNe7UQ4tpJHPpr9ae9blRaX0tr9SS93DwqlotpLMYDAHMiwAOvrqi0vlVoDW/dFkXFcqU0fuv1qFQs/vD9999//5fORPqsLd46XvuH4tb/8b9Pev5+b9J0oHkUjbYf3XJucMv4qBjXkxCSpFUpTFe/feaTv/6Pb2yt/P3gQe1jMp/L6GOfqPHQUxnbvnPMerHZjOOkewPjvqNCZX0tNOpJf5C9+15i2usWKuvrpVazv/leFBXLlUohNGuX8z8AwKyWLi4u7rsPi+LZs2fvvPPOffeiL47jcrl8373gofG7mlWrcVJPQojK6//41z+GEFZXV68+4PT0dJJmD8Ni/K7631HWwDz5sxi/Kx4avytuw+S/q2fPnoUQFip0kC/WwANM4a//TzO8MrEcAICFkpcp9N989rNPTrY+/vrDt7L/lHFIdmsAAADIo3yMwH/zWWZEDyGE8N0fmnfYEwAAALgXiz8C/903n30wPr6H8Mc/xMFwOwAAAA/cQgf47775zZf//sXJlQV8v6mehBDKP/3nO+oTAAAA3IfFDfDf/Wb/gy/iEEJ56+N/26iOGYVvT6Av/8v/eONOOwe8cgqVra1Kt7b8JFZXV09PT09PT1W8uyud7wgA4KFa3AAfQgjlrZ//6/vvvfVG+KY6psUf/xCHUP6Xn/7xN5/9z85Yfbm89a//9uFbEj0AAAAPyOIG+Dfee/r1e9c1ag/Ax1988kn/szg++eSDMRXrAQAAIJ8WN8BPpF3Brj3Nvj3o/t13v/nyf35xEp98sv/TXz99L2sc/tmzZ+POF8dXLri/c4vWHx4Gv6ub+P7778PEz3Cqxnn3itwmd8zvitvgd8Vt8LvibuQ7wHe2kNv6+GlvtP2NN9778OlPw88+OYl/9x/fvZeV4N95553Msz179qxcLt9SV2cQx/FC9YeHwe/qhv72t7+FECZ8hlM1zjW/K26D3xW3we+K2zD576rRaNx2Z3jY8rEP/DhvvPf066+/Hp0r/9bGVggh/t1/fHcfvQIAAIC5y3eAH+uff1oOIcR/+ON9dwQAAADm4oEG+Da7wwMAAPBQ5DrAf/eb/Z/97Gc/++ybkb+0i9uVfmovOQAAAB6GXAf4N35aCiGEk+qlBP/db/79JISwtWEfOQAAAB6IXAf48Nb7Py+HEE4+2f/sm069uu+++Wz/gy/iEMo/f19+BwAA4KHI9zZy4Y33/u3nv/vgizg++eSDk8E/bH38b5l7wAMAAEAu5XsEPrR3kvv1x1v9fRfL5a2Pf/31h2+J7wAAADwgORmBf+vDr7/+cOxf33jrw6dvjf8zwHycnp6GEFZXV++7IwAAvIpyPwIPAAAArwIBHgAAAHIgJ1PoAe5PmrZazThJ0zQNIYQ//elPIYqKxWKxUCpEGe1bjZN60v/nn/70p+G/R1EUFcul0YMvHXhJFEUh+zgWQdpqNuM4STv/LJbXKyXfFAAwVwI88ApJW41GHFXWJw9WabNWi9PRj9MkjpM4joprlcq0gTpN0zSuJ3FUXp+iJyFN09A+rliu5CobTv/Y86fVqF16+RI94LsFAO6JAA+8Kjrj21H5+qYdY9L7YIukXkuny+EDB8e1Wpjh2DSJa+ksB96L6R97DqXNuJ3ejbsDALdJgAfIljYb3fQeFdcqpUfRH//YrULfnlXfni+dxs1WqVIYOT4qF//xr69lVK1PW81GvX3q7GOLa1uj50vT9GXvmiGNa40ooxX34mV7dUUoFqV3AOAWKWIHkCltdZYzF9fW1yuFaGhGdBQVSpX19XL7syRuXjlOf0lUKPUODUnSmvCo4WtOfVVuXxQ9uu8uAAAPmgAPkKk/qDp2jXtUKhdDCCGk6cspzx4Vip2zdkrjTXxgaX2t2Dkybk4Y/gEAeAhMoQfyrVMiPunH4NEi70Nr2dO4dhKHEK4rIfcoikK4LlsXKltblZn6HU1y+nEXXSsm9SSEkCStSuG6afS9Rejr66UwVCk9KpbHF7Wf5MG2GzZrtWYaXv59uRQGzh5FxSgkSdZjL7Ta30fWV9CtxJ+5jCCjl63mQCejqHhVmf7hxuObjzyx8RcYLpPQvclrf1wAADMR4IH8SluNRj25nII7Rd6T4tr6TdaIR4ViFMdpSOq1aK0y973begPv0QzVynsvF9I0DRP37GWzVh+qyZcmY4rhz/Rgk0YtGajDnkZRccwLiu7LizRppaXh20/78x6uvZ/RGoNpmozrYFZBwm7zsUX9W41aPPQYrrgAAMDtE+CBvEqbnZA5tJdbL3umSb1RbI/iRqX1rdLQuOokmTcqlYtxPQkhjeu1OETF4v/2ww9/9/rfTd7D0urqH09PT09PR+rY9VfYz7RsuvtyoT13f7IAn8b1OISoWK6U2qG5+6RG6uFN/mAHJcnAw01bzZePSoWolP3Yo2IxJElGgu8+luvzez+QR+Xu+5VOF9OkXouGvuSBxsW1SqnQvv+01Ww3j2shqyBgGsfp8APrFB9Mk7hZKpSi0P1pTf3bAgCYiTXwQE61mu1IVlxbH9yJPSpU+ovEp1tePqJQ2erXjEuT5Ps//zlJ/nRyclKr1RrN1mxnT9NWs9EbDY7Gr7C/BcW19UovMA88qaF6eLM/2OJaN75GhaunLBSKmcUDJs/v3U5G5fX13qWiQqXS/r6GywMMNq4UevcfFXo1AZN6I7OcwKUH1is+mCYtBQQBgLtnBB7Ip1Z7unZULo1mve4M8ynGp8eJSutbhZHV4J3J5PGlQephvQXRIfzpT3/KPn2xfMMB22nm0BfXRkaZu8vpB7azm/3BTjLtvXfhzhj80Cr+KfJ70t12/dLz60+b6D2Y7h7tUTljonxUqpSTWpyG/qD6wB9Hn0E0v58WAMDUBHggn64qH3eDAnHZpytElUKpEk5PGz/+5Yfo9deTtJvm06ReS2ebON3O/jfu28QXzo7F3STdC7wzP9jpFgNkJPgp5s9fsVJ+pP8vewXoMh/VFZE864iJahsCANwOAR54ENI0DS9fvgxpkvTT9fy99trfvVZaXS2FyuCS6MuLyK82ppj7VF5Of4fX5OtxQ8qTP9gpq/GNJPjJ83v39id6ZZD28vuYxv1pBZcmM9jVHQBYNAI8kGNpq9GIby+uXy0qlNbXQ3s1e9Z+blF5fb30x9PTEMJoEbubGShhP3HGHJevM4eU7+LBXkrwU+T3mYx9vzDn+RoAALdHgAdy6vK+YFFHsVAovGy2NxOf3eh25KdZUTxjyfUd6Jewn6IE3rgejgzm3+6DHTA8e7/Tj9vK7+O/oZuWOgQAuDMCPJBLabPR3xdspIrcy6xDptIdl84aWh/qxz2kv25R9dn2kB+jO5Z/2w92UCfBp0krLb1sl6WbKr9nT/vvvoDovHu5tuzcNWvkAQAWh23kgDzqjkFH5awa8N0a5TcQFYrt0yb1xhU7hvWy9J2lv7TV6I6BZxaKH39g9s5n3aLznbH8W3+wQzq7yaVJqzVVfu/uQpcko5u/jUzFf9T5arIah/49WfEOACw+AR54aLrbht1MVCp3Nj1P6rWTWqP5lx9/HLxI2mo2at355NNF6dl09o+v9aawT7sFXRo3mpcjfPdtwERz8efzYIf0Enw83fz5XoKPL99Sbxf73ql63+Ro44F7uvGGfgAAt88UeiCPokIxiuM0pHGjEfXGitO01WzUk35KG1r33JkVnyattBRFaZpeM2re3SM9hG7C/HP2ju6ZG4zfRFI/uS4pR+X16begS+NaLaxV2vXv01azGY/skD7Lg73a1Y+9O4s+TcNU8+f7G9gP3VKj+0Ji6I1KoVSOkjgNaVyrpWuVUiGKLt3ULb2B6ZUT6JdSAAC4AQEeyKWoVC4m9SQNaVKvDafdqFguR0l7g7fBdc+9xdBx7SQOk4SqQmVrPRqu6DbSj2K5Muf4fq3M1ekTHFUMSZLG9Vo8/PnwLvYzPNhrrnv1Y+9WsgvT1q8rVNbLaS1Ow8gtRcW19eGvJOrtFzB6V/fyFQIAzESAB3KqUFlfLzabcZL091QrFsulQiGKQojaxeGHatAVKutrYXAgOb0+hEal9a1S2mo2kz/813/92J9FP4/N3KfUuWT7/mY5vtR+YHH3AUTFctYdTP9gr3bNY+8l+Knrz0el9a1Ca7Cn7Y5mfimjjUOIomK5Ox4PAJADSxcXF/fdh0Xx7Nmzd95557570RfHcblcvu9e8ND4Xc0scxu5Wzpqrjo74l0aaZ+vm/yuOvPMzTJnhP9ecRv8rrgNk/+unj17FkJYqNBBvihiB8A96lSNv7X93wEAHg4BHoD7I78DAExMgAeg7+Tk5OTk5I4ulraajTi9m234AADyTxE7AO5ab3+1EII92AEAJmQEHoC71t8LPiquzbCjPQDAK8kIPMDDVqhsbVXuuxOXLWSnAAAWnBF4AAAAyAEBHgAAAHJAgAcAAIAcEOABAAAgBwR4gOudnp6GEFZXV6c9sH1I+3AAALgJAR4AAICH63hvaWlpae/4vvsxBwI8AAAA5IAADwAAADkgwAMAAEAOCPAAAADkw/nx073NpY7Nzb3j86w2vSYZDdor4jefDn1+/nQzF+vkX7vvDgAAAMD1zp9urhxU+/+uVo92VupPzl7sL/c+O95b2jnqtzjaWQm7u3fZyVt1qwH+/Pz47CyEEFa2t5evawwAAMCd2tvbu8vLHR4ezn7w+dNfHFRDCBtPnn++v70cwvnx3srOUfXg0+P9w+12m+O9naMQwsbu888P+02OrjptrsxvCv358dO9wVkIx3ubKys7bStLm3tPM6Y2AAAAwPXOf/tlNYSw+/zFfmd8eHn78PluCOHoq+7M9+OvjkIIG0/a6b3d5OzJxn1093bMZwS+O5Nh983D0H5Ox3s7RwNTG0L16GAlvHnRfS8CAADAfbvRkPid6ub3d4cy5fbhxUX/Djr5/f23ByeAL7/9/sZBdTCd5tg8RuCP94bWIYQQzp/+qj1JYePJ2cXFxdnz3RBCOPqVUXgAAACmdvZtNYSw8Xjl2pZrbw4v4F5+c+2W+nT35hDg2285wsaTs94Ae/vZho0nn+8vh/7MhuqXv5XgAQAAmMnlcD7k/Pf1u+vJ/bh5gG8/pG5WDyH0I/3AzIXtd3dDCNVvz258PQAAAF5J9d9fMSb8kIbax7h5gG+Ptg++COnk96tfjgAAAMBkVh5vhMnGhC+H/Ic0MD+/KvQ93cdzqboAAAAAzGT57fc3wmDF+RBCCOdPN5eWlpb22h92Jn4PL93uVL/ryXoTcLnNwrp5gG/ffv8lR/fWh6sLdMoBTlBxAAAAAIZ1E/zO5tPjdvo8P+7sDN8bPN7+6MlGCNWDlc2n3SZ7v7hUcr0z0f5oZ699mvPjzqZqeXDzAN++/erBp8fnIYTz40/btz64AP78eG8no5w/AAAATGR5//MnGyGE6sHOytLS0tLSyk47vj/vb1feb9NtclTd3d0dOk875Ydw1D7Nys5Bdff58+E2i2oOU+jb0xQ6d7+y017/vvvLdk2786d7m90P5Xcgl05PT0MIq6ur990RAIBX2vL+i7PnT3Y3uv/e2Nh93t8MbaDN7kanzcbu87PDdzNO0z/Lxu7zs8O8LP9+bQ7n2D48e1IfmnPQfwVy9u1R+/ON3ecv9uV3AAAAZrW8vX+4vX94TZvDF9uDTQ4vLi4dsbx/+GLoLMujbRbRPAJ8CMv7Ly7ePn766VffhseP3317f3swqW9s7L7/y4+GPwMAAACmMZ8AH0LnTcjIp9uHF3mZjAAAAACL6xa2kQshhPPz8+sbAQAAABOaZ4A/P366t7nZrvW3stLeie94b2lzr1vlHwAAAJjNvAL8+fHe5srOwVF1dPu86tHBzspmO88DAAAAs5hPgD/eW9lpV5vf2N0dqOofwsrjdv3+6tHO5lPj8MArqL3/XHsvOgAAmNk8AvzxXnuf993nZxcvDg/3313r/215/8WLs+e7GyGE6sGnRuEBAABgJnMI8MdfHYUQNp6cHY7ZKW55+/DzJxshhKOvJHgAAACYxc0D/Pnv6yGEjfffvmqf9+W335fgAQAAYGY33wf+7NtqCGHtzavyewjLb66FMFrgDoCFsrW1dd9dAAAg2y3tAw8AAADM080D/MrjSSbHdxbKP1658fUAuD2np6cK5vMq8FOfymnXfXcE4FV38wA/yfL286e/Ogrh+on2ACwC/zOdV4SfOgD5Mocp9N0Ev7O5d5yx0/v58d7mykE1hLDx5KPtm18OAAAAXkHzWAO/vN/eJa56tLOytLS5+at6CCHUf7W3t7m5tLSyc1QNIYSNJ5/vG38HAACAmcyniN3y/ouzJ7sbIYQQqtVqtf3/Hh1Vu3XnN56cvRDfAQDyzKIDgPs1tyr0y/uHL87Onj/Z3djY6H+6sbGx++T52YX0DuRU+3+trq6u3ndHAAB41d18H/gBy8vb+4fb+/M8JQAAABDsAw8AAAC5MO0I/PnTvU+/nf1yjz86NJkeAAAApjZtgD/79ujoaPbL7b57GAR4AICcOj09VRYE4L6YQg8AwPXkdoB7N+0I/PbhxcXhrfQEAAAAGMsIPAAAAOSAAA8AwBROT0/vuwsAr6jbDfDn5+e3en4AAO6MZfAA92uOAf78+One5ubecf+T409XVpaWljb3nh4L8gAAAHADcwrwx3ubSys7B0fVasYfq0cHOytDyR6ABWeKLADAoplHgD9/urlz1EnuG49X+n/Y/uj58ye7GxshhFA92tl8ahweACD/vOMD7sv5073NpY6xCfN4b2lpaekhjiEvXVxc3PAUx3tLO0chbOw+//xwezmzyfnTzZWDagi7zy8Ot294udvz7NmzSqVy370AFst//ud/hhD+6Z/+aUHOc9va/Wxb/N7CzPzUJzf6n6+8/AcNFlCj0QghvPPOO/fdkdzqhM+OjSdnL/azImi72WLHz9lMuw/8qOOvjkIIG0/GpvcQwvL+50++XDmoHn11fLi9yI+wXC7fdxf64jheqP7wMPhdTetvf/tbmMd/HOZ1ntvW7mfb5L31u+I23Orvaraf+qtp9D9fefkPWib/veI2TP67agd4Znb++3oIIew+P7sifT5o8ypit/bm1c9v+c21OV0JAACAV9bG45VXM72H+QX4+u+vXt/eeVUCAAAAUzreW1paWjmohhCqByuXlrifH/cWxm/ujWyB1l8R319An9et0m4e4Lff3Q0hVA8+vapCwPGnB9UQwu67izx/HuCSdpUm+x4DjFLHDlgQx3tLK72q6qF6tLOy91VWu98/3Vw56LarHh3srOSwyt3N18CH7Xd3w9FRONrZDE8+/2h/+dJshvPz409/0S40IL8DAOTd6uqq9A4PyN7dXu5wxuO2Dy8uDtvV0Ydq1x3v7RyFgZrq58d7KztHR6MnODo4CBtPnn++v70czo/3frFzVD3a2Xs3Z3Xu5jGFfvvw+W4IIVSPDlZWlpaWNvuWlpZWum9Ddp/n69EAAACwyEZqqi9vH5492chsu/v8xX671fL24edPNkIIR7/K2V7ncxiBDyFsH549f/yLnYP2dIRqtXrp7xu7Tz4/zKzvD8DC6C0ZMLYGAK+MWYfEF0Inv7//9mDYXH77/Y2D0VD65KPBAeXl/V/uHuwcVb89CyFHSXU+AT6E5e39Fxf75+fHv/30qy/rvYJ1a2vvP3737f1XtMQ/AMADdnp6qkoIsAAu74m2/OZaCJcD/MjGaSuPN0Ko1n9/HnKUV+cV4NuWl7f3D7f353pOAAAWi6k6wEJ49TY7m9c2cgAA+WCDCYAHYvnNtfvuwh2bdgT+/Onep9+GEMLjjzqL2nufTKJ3FAAAANzc5WnwmQPzI41++2U1ZMysX2zTBvizb4+OOlvCHbbX+vc+mUTvKAAAcs8yeOBetfc0r3752/P9/khxJ5pfkt0ob1udm0IPwGXt/zlugStwBbkdWATbHz3ZCKF6sLLZ2Q/u/HjvFwcZ+T2E6sHK3vGlRnnL71MH+O3Di7benu69TyZhJ3gAAADmZHm/vaN79WBlaWlpaWll56i6u7s72nB3dzcc7fQbhbDx5Cx3AdUIPAAAAHm1vP/i7Pnuxkb7Xxu7z88O381q9+7h2fPdbquN3ednL3JYn+3m28i1i9hdV53u/Onep1/W6+9/nseHBAAAwH1b3n9xkbVr+fL24Yvtw4EPDi8uDrObZX2eIzcfgT/79ujo6Ojbs6tbLb8ZjqrV6nXNAADuinIP15pkyz0PEODO3NUU+sxC/gAA5JY6dgB3bIYp9Md7Szsj+8Yd7SxNsJfcxuOV6a8HAAAAzDACv334PKOm3yR2f2kBPAAAAMxipiJ22x893w1fdf5Rrx9Vq2FjY3dt7YpDHj9+9+23t8V3AIAH5/T01HR6YGFtZ5e0y6XZqtAvbx/2Nsw73jvaqYa1Xx7mbgs9gKtMUroJ4BW3urqqiB3Anbn5NnIrj3d3d4PF7QAAAHCLbl6FfvnNcFQP4ex8Dr0BeKjsVgUAwA3dPMAff3UUqkcHX9nhHQAAAG7NvPaB333XCngAgFeVGUYAd+DmAX7l8UYIof57U+gBAF49in0C3Jk5rIF/+/2NEKoHv3h6LMMDPBQW7QMALJqbV6EPy29//jz8YufgYGflIGxsbKxdsSH8448O920GDwAAANO6eYA/3lvZOer9q1qtVqvVsY133z0MAjwAwMNzenpqOj3ArZpXETsA8q09W97/+AZm4D8dAHfj5iPw24cXF4dz6AkAAAAwlhF4AAAAyAEBHgCA+bB1BcCtmkMV+mHn5+M2kzv77ae/Cu++2N+e9yUBAGazurp6enqq+lqmqUpjtJ/kLfcI4FU3pwB/fvz0018dHI0vP9+x++58rgcAAACvlrkE+OGd5K6w8XgelwMAmJENFwDIrzkE+POnv+qm942N3bW1UK8fVatD/3cIYWP3+eeH2/aABwAAgFncvIjd+W+/bCf0J2cXL14cHh4efv7+Rgihuvbu4eHhixcXF2dPNkKoHv3qt+NWxwMsGmN0ALOxEh7g9tw8wJ99Ww0hbDz5fL87vL789vsbIYT67zuBfXn/xfPdEKoHnx7f+GoAACwkLz0Bbtu8tpFbe3Ngdvzym2shhOq3Z71Ptt/dDSEcfSXBAwAAwCxucx/43hB86Cb4wU8AAACAid08wK88HpovP/DZ4BB82+gnAAA8LJbBA9ySmwf4znz5LwdL1LU/G0z157+v3/hKANyl9nJW/0McmJxl8AC3ag5T6Lc/erIRQvVgZXPv6XEnsXeG4Hup/vjTg2oIYePxys2vBwAAAK+eeayBX97/5W4IIVSPDnZW9o4HPqoerCwtbW4uLe0chRDCxvtv2wgeAACAO3e8t7S0tLR3HEI4f7q5tLS0+TR3NdrmU8Ru+/Di7MnuxtBHHz3fbf9f1Wr7/x3caQ4AgIfL6huA2zC3KvTL+4cvLi7Onn/UnSS/vH14dvZ8d2NjY2NjY/fJ87MX4jvwSrOkHHgVWAYPLKrtw4uLi4vD7RDC8v6Li4uLHEbUOW8jt7y9PLgf/PL24YsXL168eHG4v527RwMAAMAiOT/e21xq29w7Ph+ZC3+81/5nv93m3tPzzuz5S1Pm28e259QPHL20dxzOj/c2+9fp1Xob6sjTfpOlzc29jDa34Db3gQcAAID5OH+6ubJz1FmiHapHOyubn36b1fDbT3/Rb/d42kps9V9truwcVfvX6dV6G+rIQb9JqFaPdlY2hxvditdu/QoAAAAspL2wd5eXOwyHMx97/vQX7b3Nnjz/fH97OZwf7/1i5+goo2X16ChsPBlcxH02zYWq1WrYfX52uL0cOhephqOvjg+3t9t/P95bOaiGsLH7/PPD9kzz8+Onv9g5qB7t7L3bnqJ/a4zAAwCvNPUpMrUfyE0WtHukwFyd//bLaghh9/mLzvrs5e3Dz59sZDe+WQX1jSdnnWQelrcP23uu1X/fmSJ//vRXR+1+HPbWiS9v7784e7IRwtGvbrmwvRF4AADmaXV1VXqHvLjJkPidauf3jScfDQ5wL+//cvdgZ3QQ/oYbmK+9OXj0yuONEKrVb89CWB7TjxBCWH77/Y2DavXL357v32JtPAEeAACAxXb2bTVcjtahm67ne6mNxyvXNakerCwdZP+lE/RviSn0AMxhriwAwKIYyfnz036RcG+MwAPMw95eCCEc5mQSGsA0frf3uP1//MthZrlngLtS//15WIQdyodL5N2haUfgL2+zF0II5+fnd7LlHcDdmH04eu9O67gCLCylAYE5W3mcWa/uzgfE2/2ofjtVXfv5mTbAj648ON5bWVlZuYMd7wAAAHglLb/9/kYI1YNPh5Ln8VdZ28iNyordnbL2M/Ujq9z88d7SyGD33M22Br5XQh+Ah8wYGgCwGLrJeacbkc+P9zYzKtBnH/zmWgghHO3sHZ+3j326uXIw0+D98v4vd0MI1YOVzafH87Mi0wAAIABJREFU3Vzc68vuL293Zv20a+A7JfQPfvH0zV++vTJYnG+iefTLywuwXgEAgBn8bu+xZfDAfVne//zJlysH1cEK8Bu7u+HoaIIgvv3Rk42jg2o42lnpZv7d58/DzqRvAIbOdfh8t75zVK0e7KwMlaLf2H1+eHl3uTmbdgS+89ojVA92dlZWVlZWVtp3fNT51zXMtAcAAGAGy/svzp7vdtfCb+w+P3vx7qR7yC3vvzh7MnTsDbL29uGLs+dPdjd6y/I3NnafPD97cdvxfYYq9Mv7nz//9hc7k7zkAAAAgLlZ3j58cTGw7U97hLhfpG378OJi3K5Ay/uHL/YH/7h8uXHm0cv7Ly72Mzqyf7i9f+cbEM2wjVz7kZ2fn4ezs7MQfv+rnYNq2Hjy/JdvXn/s0KR7AFgErcZJPRn71yiKQlQsl0qFaJ5Xi8rr66X5nPHhS5u1WpyGEIprW5XCta3C1Y/3x++T5Ps//elPd/YVTPOV+3kAjHHeXrS++/xicJi7XcRu4/ErkzNn3gd+eXm5s6L9qxCqYe3N7e1bny4AAHcuTdOQxvUkjorlSiUfoSptNRpxVHkwETAqFKM4TkNIklalMCbBp60k7f8jfRlCxt2fnp7++Je//Ng+a3TPj+ehfU0At2r5zbUQquFoZ+/ds8P2VvDnx3s7RyGEjffffmVKrc1WhX7QyuPd3d3dV+eVBwCvqDSJa7Vmen3De9ZqnNTqyeL3cxpRodgOuWk67saG8nsISdLKbtfL78Xi2LH8u5Dfr+l3e4/vuwvAq2n7o+e7IYRwtLOy1Naux7bx5PPbLfy+UGYege9Z3j+884n/AAtlb+++e8BcZM7PTtP0ZasZx+2olca1RnTFLG5uSRRFIaTjR9b7+b1YLqdxnI4drP/hx4XI77n0L4ffSu/APVrePjx7/vjTX315VG1XZNvY2H3/lx/tb7868X0eI/ADzs+Pn+5tbm52XogsbW5ubu49PZ5kfzkAWEhRFBVKlfX1cjc1JnEORuEfnkKxGEIYO7Lez+/FUnTVYP1f/r+/dNrJ7wD5s7y9f/jixUXHixeHr1Z6D3MM8OdPN5dWVnYOjqrVXoH6arVaPTrYWVlZ2nwqxAOQY1Fpfa2dIEMaN8fMzuYWPboqlw/k90I366dJa7Sl/A5Art18Cn0IvYqAbRsbG2trayGEUK/XO3G+erCyGc5evEJrEwB4aAqVtWJST8KYUmppq9mMk6QXL6OoeG3t+rTVaMTdQ64+IE1b/z979xsayZkneP4nj2un3UnuUOOScHW1OSVI1iHpYHe4lm9SJI0Y8CJ5xvYLoxem1nUvjIJ+M5KXqaGPcnNztIst1stYujfdkcyLLW/hAdEHttstMYYZMSTKWauGZQ4kcbK0l2Lbbpuscps5kdUzVca6F5EZGZkZGRn/44nI74eZxpWKjHgiMjIyfvF7nt9zr2P9ksvluqrjW8uwS6O2t1OT3nrmftpp33Z367GWVZeO93jbcquQXaN+rzHeVX6uI343svX1ut2Sja8fNTf97d4tfP2b04M990fGzSdi/0Y3H5OxW/4PV7Qq2nRJP0y6FQAwjMII4E82rhnRe3F963ZPH4aT7Y1rS2tVqa5d23ieEB5Adum6aJpomlAZJLO+3RyILY1GQ6yBVEdI1nqtUTdq18/O2Y6Zb9w72KnVbd7QG8k17h0c9NY7azQajdp+vd5vA3bb9NFOb+up9y/Vf+9gr9axE613uNywGcH3DIPvit/ND6pnycY9o4Bdbqw7DP7ub//j3q/cH5nQPpG+ek6P1uGKaX65o6MjEZmamup6nWHwAJCsEAL47bfWqiJSXLfPsE8sru4ey/zkWrW69tb2qs5kcwCAlDJnM+uIDM1oNjdWaGdIG417pwf79Uajvr8nNgFdo15riOTGZmdmjHe0YsKeOnmN02asaFnYEkI26vsHY8byufG5hfG+M4n7a2evdvSeG5udGR/N5TpWU9sTuzJ/jVqtIbmxwsy4kRRv3Ds92K81RBr12un4qJuYtFXIrrsDRE/8bn5QXUs27tW/FhF5vCt+N/fo8Sfy/+P/9D8PPDLuPxH7/XD8mJorqxuHq/VBtQ9X7eB0lInnAGBoBR8Df/LJvgyq3T+xenu9KCL7nzAUHsAwM9JZRmoLGdE4PTCi2bHZuRlL/+ZcbrRV+q7Rp+5drjA3Z4Z/0l6+s07evdP2+i1xZ250pj0qv+/caqG0s1OrPc3W53pWI/X9A9siAWOzczNml/bc6HhredvB6nZahew697c3fm9PO9dR8q614ONPjHY+2GgemScujl3MuzgyIX0ig3R8UH4OFwAgg4IH8MeHVRGZfca5b/zEM7MiUj08Drw9AAASZwZoraBwbNYu45obnzECQLu6d2OFnjxqbrxg1F9rL3+vXhcRyRXGe9ffruv2YGCD/bezcz2ntWZ7bDrKt1ZjW6nfZg9y7ttvaJWnsy5vF79bV91uyINGM35/orNbffPIXLz4RO8WbY5MSJ/IAGEcLgBA9oQ6jRwApF+/kZ9Di14DtsxYqhX99StqbhdIGuzf0pNkHp1ZWFhYWLDtNJ3L2bxoK0A7Oz0wK6rZbtohxrR7x7dd70DnGyyJdfv4XWxK0TcDb7nwuHX8YPvI2ITv1naH/IkMEsrhCoumicgdvWR9jZHwAJCI4GPgJ6eLItXye9v6osPw9u33yiJSnJ4MvD0AAJLyoDu8bb1Q39+p2yzf1lNPzbYOusPy5usNefDggTTq9Xqj7rqjtv929m5exKH1rTp/3WX+Bu2vS+1Cds3V94vfewvZteL3J77VEam3j8yvRETkV7/6lf2mQ/5EBgjncEWDOnYAkKDgAfzEM7MiVSm/uXF9sd8w+JONN8sigzvaA0D6aJqIUHl+OJhJ2BDCq365WrPSfceGrdPNqaBvpjln1/ywN9yeS65//N5dyK5P/O6Xcp9IlCpSERFduMoBQPJCqEK/eH29WF6rVtcm5w+3bl9fnOgI0k9Ott+6tlQ26tRfpwQ9ACC1zGixdxaysVmHquP2K+tOUTf1JPm7p2zLNY2Njo4+ON3Zd86od/HeTnv9Wh9K9TZHrTneGw9Ecg7xe1e2Xprt6hu/j81+5/Ffi6vhM6F+IgAAeBHGPPATq7fXNyfXqlItL02WRaRYLBp/qVar5lLOdeoBAFBcq/i4Nf/ct8d4UK0cv1kivWPGshbXxczCamfOXE+fnvYDxsiHwIzgG/Jtp/i9I1s/Ks2R7j3xe/vIfP07ru6KQvpEsqCiTZf0w6RbAQDDJZwidhOru8dbK8XWP6strReKK1v2k8QDABJH3T43GvcOWplVa31ws8JZv6m9Gqd7Ozs7Ozt73WXZ+7yjVeJ8rDU5fL05Z1tvrNge1j2Y/3Z2adVR65ifrbdFUQ7hNprQqN9zzL+LWIvWN58r2FSqax+Z33xtu5KuIxPWJ5JuxO0AkJTQqtBPLOq758fHW+srK8W2lZX1rePj8119kegdAJBGjca904O9vT2zY3Tn7G+j483JuWsHdqGvOWV475RxdlO2tZ4S9PbRt2tZc0Y3V/y3s1NrojvbmeLMFg1cTRDNOd4bjdOGc/zejuBrzbnv7B4rmEfm7NdnNiG86yMjHj+RYcAcFgAQujC60LdNTCyu6ouroa4TAIB4DCzRLpIrzHWNIc+NzxTqe7WGNGp7e41CYXx8tDXD3OnBfrOztd2U4SL1/Z1GYXZmvFlO/fS01jPHujmOu3ZwkDNTvo3GvdOD/Xo7gO7oGf/tjjpvjUYjl8sFamen0fFCrt5az+zM+KiRELc0ydVqAmh2ja83s9394/fWsWgOzM+Njeb++dPe1bWOzNdn9frXud9tOB4ZP5+IQ9O6PyYAAByFG8ADAJBZtmOeRSQ3Pjcne3u1hjTqtf16rfvPhTm7KcNzhUKuVqvX9vc635Abm7UunhsvjNX36w1p1Pf36l1LFgq5+n6te0i6OU69trdTk3bhOn/ttDkOlvV0t0lyY4WZmQjT7yJiDoM3OMXvZrhtNG00J73xu1j36Ovf9Hwg3UfGxydi37Q+H1O6MAweAGIWWhd6AEBzMjljYjlkRS6XGyvMzs0tzNlF781lxucW5mYLYx1JVON9C32j4tGZhbnZsfY7crmx2bm57gy/jM7MGavuWvHc3Mz4qNmh3TokfXRmbnbMulEzMeyrnY7727Eao1Ex5JGbXeNFBsTv1mJ6zsMScuNz3xm7mH/icRdHxvsnYr8TfT+mVCBuB4BEkIEHAAy70ZmFhZnAa8mNjs+Mjnvc2ujM3OjgTRurtl3Ovu250Zm5frvktp0BGtXB6ejmxucWfDXF/Ufm4cN9/In8xSfcVXP09on0a4PdxxTJ4QIAZAYZeAAAAAAAUiAtGfiP337h5s7CjQ9ef9bur5/97O3/UNmpGYPWCguvvrL88rNXYm0fgCFl9JY3es4DAAAAUUpHBv7jt2/uOPzxhR+804reRaS2887NH7z9cSwNAwAA2cCcZybjILgbTSAVbTri5gAA2tQP4D/7+O0XHMP3mzsiUnj1xk8++OCDDz74yY1XCyKyc3P1Z5/F1kYAAIAhQx07AIif0gH8Zx//7O3VHzhE7/LZz97dEZGFGxutPvNXnn1548aCiNQqf0cEDwAAAADIihAC+G1tZF7b2D4JvqYOn/1s9Qc339mpSWHhxk9uLNgv83eVmogsFDtHxj+7/GqBCB4AwkLXYgAAABUED+C33ytLtbz23nEIrelWWHj1xk82Xu9bkO7TX9rF7yJXnh4XkdovP42gTQAAAGhjGDyAWGxrIyMjI9q2iIicbMyPjIzMb3hII/t4i4LC6kK/8tJiSGtqufLyxgcbrztWk//sl6ciUnj6uz1/+e7TBRE5/SUpeACeeCrdBCBd+IL7oWkickcv2f6RYfAAELPg08hNThdFqvufnMjiRAgNisGHH37Y70+1djF7JajWHmQD55WDs7Mz8XKILp2dicj9Wu3GpRsicvP+TfOVsDYRA2uTaj9+TkQKP/rIYRlbSu0RMiPc82rgaazC1/PLL78UkSeffDLBNliPw6Wzs0ffenR2dla7b39YHj16JP0PmgqHtJdq7UE2cF7FbGJ193w16UYkIXgAP/H8cnGtWl27tvHM7VW1YvjaLz8V6c3g/+Ef/qHt4h9++GGhUIi6Ue7VajWl2oNs4Lxy9vDhQxHxcIjyeRHJFwp5yYvIrfwtvfVKaJuInrVJn164ICKf3nq+K7Hm3GzOK0Qh9PNq4LdPha+n0YaHDx8m2FOg4zjk8xfkQj6fL+TtD4tx0eh30FQ4pF24XiEK7s+rg4ODqBuDbAsewMvE87e35NrS2trS5JoUi8XZ2dm+y05f11fji/HtOtcDAAAAAFLtZGN+cq1aXD/eXZ0QkW1tZKksK1vn10W79ma5WhURKa6sv3HdMcdsrEXM1Rgvbm+89eZmcx1SLK68cVtXJ1EdPIDf1iaXyua/qtVqtbmrdlZe0kWZfQeAYDRNRETXO17UddE00bTu11umpqaOjo6Ojo4YiAtAfRWpiIgu9he09mLaNOPhASRu/835SUswWi2vLZUPt851+3Jtreh9ZcsavRsvtlWr5aXJ/ZWt3T5riVsIGfjkXHl6XKRm11HeKE8//rRDBTwAAAAEVNIPqUIPpNpHRkIiLs/1yXCEolqtysrWsb44IXKyrV1bKlel/N62vmgTe29rk2tVkc7IvPlicWWrlXQ/2d64trRWLS9pL/V7EBCv4FXoF/Vz90Le537F5vuXpwcAAAAAZFFx/bjV231iUX9jRURk/5PeeeNONuaXys3Fra++WZZmTN9KyU8sru4erxdFym+qMf9cqjPwcuX3S4V3arV3Nj9++XXLXPAfb75TEymUfp8EPAAAAAD0FWlKPG6zz1jHaxvzpVUPj6VzGLeRZ+8c+C4iJ7/YrIoU1693J52bZds3f3GyGmNBtz7Cmge+y8lJPI8nrvx+qSAiOzdX3/7YSMN/9vHPVm/uCPE7AABIGWOm+pSiIz2AxBWnJwcuU12zlnCz/ftIN2NUfPXwOLSW+hdmAH+yvaHNzxu7ODmpbYuIbGsj89rGdmTh/JWX//TGgojUdm7+4IUXXnjhhR/cfKcmIgs3Nl4mfgcQD01iHTwGAEqhfB2AdCmurBRFqmvXOjvFHx/2L8aujrAC+JNtbX5yaa1sU4G+Wl5bmpw34vkIXHn29Z/ceHWhPe9iYeHVn3xg7VEPAAAAAIAxxF2/vV4Uqa691RukFtePYyro5k84Afy2NrlUbs61t7K+Umz/ZXK6WBQRqZaX5gOM+n/29Q8++KBvVH7l2Zdf3/igZeN1cu8A4JbRZZc57QBEwbi2pHpoAICMMbrZT6y+sSIi5SVLnnlyuiiqdJTvL4wAflszRhGsbB2f7+r66kuz7b9NrO7uHm+tFKXPAw4AgJIYFwAAALJrUd9akY7i8hPPLxfFttz8tjYyMjISJCEdnhAC+O33ytJRsb/bxKLRRUHK7xHBA0AqkUbDMOA8l2C9cqhjByBNFq+vdw6Fb6blq2uT8+0qbifb2vxSWURW3ki+BL2EMY3cySf7IlJcft5pd5qV98vvbeuLKowcAICgNE1EJEszrwyrewc7+3UPy+cKc3Pjuciao6jG6d5erSEyNrswMzpo4UGHNJfL5cYK4+OjCh1GcwcvfufxJ5JujIj85qtf7ez8KkVnW0k/JHoHkDYTq7fXNyfXqmtvba8a49sX9a2V/aVytbq2NLlmXbS4sqXECPgwMvBGsb7OGfd6TTwz6/h3AAAwHBqNRr22v7ezd9pIuilKoSAFAMStdyj8or57vLW+UjTLuhWLK+tbx7uKhO9hZOABAAC8a9T29iQtGeY4TU1N/cN/+VXSrQAA1Szq5+dm18eJ1d3z1T5/67NM1z/7vnFicVVfXFW1k2XwAH5yuigysHN8c6D89GTg7QFARPylv4xib7roxn9roum6LpommpbqDvZXtYqkuPkejM4sLMx0veapwzj66Nv9u3Hv9GC/1hARadRO741zjHs9cfE7T1yUqanxhNuhaSJyRy+VXL+jok0zLTwARCd4F/pWsT6nAnUnG2+WRQZ3tAcAAFmXGx2fmys0Q/t6/V6yrUFYiNsBIAYhVKFvRfBL89q2TWH9k21tfnKtKiLF9euqjBwAAPQyalBxF44Y5EbHmhF8o8FIeAAAXApjDHyrel+1vDRZlmJzwP/+m5r25n65Wm0uVVy/rUTdfQAAQtVo3Lt3WqvX24GoTZl1szJ7T8fyVn99tz32XW3OsslcYW5uXO6dWt6Ty431rwLf6F60MBNFH/dcLidiG7pbd/D09FQS38HON/Rfe8fIi8a909Nard4w31KYmWm+o2t9ubFC79p+89WvvvqN/Ooff9tmGELPDqhW0h8AEJ1withNrO4ei3ZtrVwVqTZD9mq1XDUXKK4f7xK+AwCypnHv4GC/3h2HNhqNRm2/Xh+bnWuFhqMzs2P1/XrPqO/G6YERvcvY7OA42fXmOtw72Kt1vKnRqNsv336YYFl0f68+Vhgb1DSvzMR7LmcJPaPewa/P6vW9jvJwA3aw94i01l7L9WuONO4d7HdOpNfcyuzCjBzsde2g0db82O/kHx9cg8P2E3I6NklgGDwARCe0KvQTq/ru89e3f/HWm5v71XbavVicXX7j+uoiwTsAIHMap81oMzc2a+ZX20Foo75/MLbQE8JbXvUYvnvZXPtdtVpDcmOFmfHxXE4sJeQa9drp+Oi4tZdAMza0rN9Ye73mNK+7H4179VYC+dv9drBeqxUKhRB38Ddf1c++9rCDZrTckSRvNO6dHuzXG436/p7Yhc312r7NJhoi9f29hjQatm09+/VjT4zlBxw0M3rPFWZnjPY0V96o7+/lkq/oz2zwABC1EMbAt01MLK7qu7u756bd3V2d6B0AUqYkJWkV2Ed/905bXd/nZiw9mHOjM3OzzYRuxwDv0fFm5bb6/sE98Rq+e95c29js3Mx4K9FtKSHXqN9rL984rZl9/Nvrz43OtAvOhaLRuHd6YKaRc2PtXYl8B89+I+J+B82PZ2x2bsbSRT3XfkujXrOdy75nEwWz/b1tbe7b118/sts1i9bxyRXm5sz25EZnZpo7WzulHiAAZF5E88CfnJzIxASBO4CM0jQRSfUscQjBvXoz4B3vDb6/3Rzh3Wg8EGmHfuMzhfperSFSr5+O1Wtewncfm2tttfcdud7lzei50JPFzY0Xxmr7nnPwjdreTm3AMtatRb+DX4uIPOFyB1t9BOw/ndZHaTsPXu8xHB0bk3rdvq2tfXv09de927FoHp/etZvNbzQaEu5Y+IpUpDVNJgBABWEG8CfbG2+9udkuWyciIsWVdbrQA8gq6yTwGDp2M8i39CvRZobw9dp+8yV34bu/zbX/2OXb3W9opbbHxuwaY8afYTI6mVs2EssOPvEtdztoxu+2B0Sszwi6o2brqAAXbXXcN0uDHD4hp0MHAMiUsAL4k43mXHHdquW1pfJacWXrtk4UDwDIrkajIQ8ePJBGvV5v1PvOjdbOwouI+/Dd7+acw8m2B86TufXEwwH0qSnfrdF49OjBvXv3EtrB1vL1/R3nJxe9nQLsHigE12yQu71NHHXsACAioQTw1ui9WFyZXZ5+6ZlnRD755L3DzXK5KiLV8tKkbJ3rzAMPAMiUxr2Dg5pTeNkrNzqWM3vP98vvhrc5T8IMD3smzHOjawc/+yy0vP+Z0ainJsNaIWxRxw4AIhVGAL/9lhG9d88Vt7i4KKu6frK9cW1prSrlJe0lQngAkKmpqaOjo6Ojo4FTRiklpc2OUveUXrmmsdHR0QenO31GjbdL14lId5308Dfnje0Y87h07+CFCxcuXryY8A6OzfbWvY/IpXG+WQCAAYIH8Ccbb5bFaab3icXV3a3DkaWylN/b1heJ4AEAWWAG4h2TurU8GPSusbGxer0ujdrB6aibRLW/zbk2oI/8gA7oYejdwZoxjZzRgKCr//bjj4v0rxLXs4Pf7jvEPVn2TyBazz5ifNwAAEhE8Gnkjg+rIlJcft5phPvi9fWiiJTf2w68PQBIAaNAvcY0bBnWKnKWK/SG0+2K4d1vMoPUwvjMeGv2rwPbqchC2JwHudGxnIhIvW43FVmf2dvCFPkOPvHE4yKDd/DK1JSIHB192hzI3jETXcdbTvd2dnZ2dvZcfHreXTGmhP/n++Yro2PGdHN2OzCw4p5rR0dHIhJKLxs60gNAFMKaB372GecKdRPPzIa0JQAAFNeaUr1beyLvmfGc5MZnvITw3jfniRnBG3PUd67/oBZ5AO8klB18vBXBu9zBUecHLA7T7kXEjOB75p432xI4fg8H5esAIDrBA/jJ6aKI7H9y4rjUySf7IlKcpnQMgMzonUPO+G/j9XTTdRG5qlWSbofKWgFvo3Zw0M7RNhr3DvZ2LCO5Lbnrewf7XRN558YLRkw2OIT3vjnPO9R6nlDf3zs4bW2jce+gc2B6VKLfwcfzv5tvhvCudtDygGXP8gaRxr3TveaIfNtJ66MyOjM71mpPR/sTaAsAICHBx8BPPL9cXKtW197aXu1foe7kF5vVgf3sASAVjI7xui4ZCNQRQG68MFbfrzekUd/f60wQ58YKhVx9v9awDFluh+/WieNGZ2bH6vt1GTwW3uPm/O3R3Gxjb7/eaNRr+/VaxwbGGrWIw3jbHTw9bW4/lB18PD82+8Q/ut/B3PjcnOzt1RrS/Qbjz37K7AcyOjNXaOzVGtKo7e91NCc3NhtzWwAAiQihC/3E6hsrIlJemt/Ytk3Dt2aZW3nDvsodAEA5JSlJNnoTRGh0Zm5utjBmmfU7lxsrzM7Nzc2Mj7aS68aQZfvw3VjJbCsLf2o3OtvX5gLt0tysZRu53Njs3Fw8hdF6d/BC4juYG59bMBpliY6N474QUsR89Jkxw91vu1m43Z7OxsT0Efky1SwrcJR0QwAgC7xm4E82tLdsBjatFKVcra4tTa5Z5oGXT95773DfmAhepLgi72kbkzpBPAAVhVi6KUWGc69dyI3PLYy7W3J0fGZ0fMbuT6MzCwsztv9wWjCczQ1cr8Me5nKjM3Oj3W90fUQGbHiAjh2s1WqFgrnV+Hbw17aNcmz3oG06tvWJi9954mLX13B0ZuE7jx+JbW8Dh7MgXJomInf0UsnvCiraNOPhASBcXgP448Nyuey0QLVarlbFZpFquVyVlZd0IYAHAADIsJJ+SBV6AIhCWFXoAQDpZtxtky4DAABQltcM/KJ+fq4PXgwAAKBpZ2dHRBYWFpJuCAAA6Ra8Cj0AQDRNExFdz8ATTqNqXQZ2BHDUnk4CEfk86QYAQAbRhR4A/OidBF5aYXwmZGZHAEfZ+c4qp6Qvi0hFu5h0QwAgU8LMwJ9sbx8PWGRycnGCInYAAADCTBAAAI/CCeBPNrRra83p4hytbJ3rBPAAAACZRtcGAIhEGF3ot7VJV9E7AKSf+3GzxjLZ6qBr5AmNnCGAbKtIRXoGCnlBfQEACF/wDPzJxpvGrO/F9a3bz086L0z/eQAAgMGmpqaOjo6Ojo5S2ME+Uw8uAUApwQP448OqiMjK1u7qYuCVAQCUUZLSHRFNtAApOADDiYsGgLTZ1kaWyrKyda4rHdaGVYV+5SWldxMAAAAAgHQLHsBPThdDaAcAAADSj/7zABCh4AH8xPPLRZHye9shtAYAUqFrEnjr9O/Gi1oGbmF1XUSuapWk2wHELbZijarNIRdee+g/DwBRCaEL/cTqGysi5aX5jZPgKwMAAEBKpf/ZJQDFnWxr8/MjLfPz89p2RxS6rY2MjIxo2yInG1pzwXltY7s3VD3Zbv19pHslKgtlHvhF/XhLJpfWJkfWiisrsw5LTl/XVylEDyCTdF3XNE3TtKwkn7gRB+BDRq6AAFR0sjE/uWadv7xarVaXJsu9ledpf6DaAAAgAElEQVQ+2ZhfMpeslteWyocdyxgV68zVlJcmZWUlwpaHJ5QAXrbfak4lJ9Wy44zwKy/pQgAPAACQOTz1A9JJi/fLq/t+zHeycW2tasxevrpoBJUn2xvXltaq5Tc3ri9aE8XltTVzuZNt7dpSuVpe0l5qhfDb2lJZRIorW7f1xQmRk21tcqlcttmmekLoQn+yMb/kGLUDgOLiH4ka2yBbAPBD00Tkjl7y+DbS7wCiY8xfXlx+ftEM1ScWV3fPz8/Pd3u6ea9s7Taj/IlF/fZ6UUTKbzbHfG+/VxaR4roRvRuLHK+npDR78Az8yS82jei9uL51+/nFCRLsADLLeESt6ySagOzQtADpIJi4KgKplZpr4OR0UaRaXbumPXP7unPcWVy/bu1SP7H6xsraUrl6eCwy0Yrfl5+3rmHi+eXiWjUFaengGfjmg5D1491VoncASKeKNi0iJf0w6YYAkVCt3rso2aTA0hIDAEgpY/ozY8T6ZLM4nV11OhGZfaYrMDXmPt//5KTvIhPPONVyU0cIXehFxOYQAUBGdc0hly1GDi2TuwYgOqTfAcRhYnX3eGulaPZ1r5bX1pYmvRaRP/lkP4rGxSV4AG88zAAA9DD6pMVcGyZUJSlJNqa1BxAtHvwBiMPEor67e35+fry1vm6G8tXy0jX3U5qnJdXeR/AA3ujJYFYEAIDhommaiOipGT8GAOHiAR+A+E0srq7qu7vn58db60URqW7+whqOWvvKi5h126zdxnsWSUliPoQu9BOrt9eLUl275qnnAgAAABykZ7oK+yeYJbksIppo6dkRAAo72ZgfGRkZ0bY7Xp1YbI6M79AVzzfj95WXFkVEFl9a6buI+kKoQr+hvXU4W5Rqtbw0WZZicWW2f5+E6et6T4V/AEAS/NXQmpqaOjo6Ojo6ylbxLQwZc0aJNI9wAYDh0qwTX16an+6cCH6tO7kuItW1Se2Z49Yk78YizfhdZPH6erG8Vl2bnJfj3VXrIikQPIA/Pixb57yvVssO1fdXXtKFAB4AACATHJ+A6IeiXbyqbaZnkioAKptYvb2+OblWra4tTa51/KW4sqVbp42TlZWVcnlp0hKmFteP24u01zQ5smZ9S1mUF1YVegBAcyR8pVJJuiHBGXfbGdgRQEXZmkOO4BxAXCZWd4+31i116KVYXFnfOt7tDN9F5CX9eGulaC6zZWTau9Zkrqi4snWsvxRly8MTPAO/qJ+fc+UGAAAYMgxAABC7icVVfXHVRfw5sajvOsapE4v67qJ1gXTEtWTgAcAdTRMRTRcZNAl86U5Wpl7TSyJyVSMPDwydilRk0LVOREi/A0DMCOABAADgVfqfUQJACgXvQi9ycuJ6+riJCUrYAQAAtQ3hbAu+huWTfgeAuAUP4Le1ySXX1fpWts57CgwAQGppmiat2nUA0seYSU7TKJPuEel3AEgGXegBAE5KkpUh/YAaslKCnkceAFS0qJ+fn2c5aRw8Az95fWurX8n9Tz5573CzXK6KSHF9641nZHIy8PYAAACQHB7nAUBiggfwExOLfQe2Ly4uyqqun2zMT66tvfnJ8e4iQ+ABDBc66ALIIC5oAJCMWLrQT6zeXi9Kde3ahutqdwAQl6x0Zw3ucxHhvhyAMWvmHb2UdDsAAN1iGgM/8fxyUaS6+QsieADp5XpiZADIKvrPA0CS4ipiN/HMrIhUD49j2h4AJMOoSF+pVJJuiAcVbVpESvpy58vGc4o07QiQPUbnIKOjkDJ4iAkAiYkrgD/5ZD+mLQFABOhQCgCk3wEgafEE8Cfbb61VRaQ4TRV6AJkxtJPAK5kShNIWFhYWFhaSboUS0l90Y+iueACglOBV6E82tLcOnRbY3y9XqyIiUlx+nir0AAAAKUT6HQCSFzyAPz4sl8tuFiyu314lfgcAYOikP+0MI3on/Q4ACYulC32xuLK+dbxL+A4AbXREx7DhbB9CJbksIhrZewAISfAAflE/H2R3V19dJHoHkGqe5pAr3SlJNu5Z9ZKI6JpINnYHAAAgzeKqQg8AAKACTRMRMctPGv+h8XwKAJACBPAAAAAAAKQAATwAAMgydUroqdOS+OiHInJV26TqBwCEwmsV+oGTxjmbvq5Tyg4AAAAAAM+8BvCuJ42zt/KSLgTwAFJG00Tkjl7qfE0TEV23qWmn67qmaZVKpVQqGf8WTRNNE7uFk+IuE6iLaCIVkZLjYkAGTU1NHR0dHR0dDUPCvOuC4KlmJwAgTnShBwAAAAAgBbwG8C4mjbM43lovWt5cXL++GGbjAQBh+VxEhIQbAACAwqLLwJ9saPOTS2tV41/Fla3j813GvwNIJzqUAr5Rtywgyr8BAEyRBPAn29r8yORa2QjeiyvrW8e7+iLBOwD1DGNRaCAJRKGpudrYVP3QRIQeOgCggtADeCPxXrYk3nf1VYJ3ALBBSAMAAAD3wgzgexLvx+ck3gFAbRVtWkRK+rLzYsbwAa2ZiOPRAwAAQAJCCuBPtu0S78TuAAAAAACEI4QA/mRbm59cIvEOAAAAAIjMyfaGNq9tx/XGbW1kZGTEz/aiEyyAP9nemB+xJN7XSbwDGAaapomIrlPSCcgE47usaUm3AwDgaPstM28c0xvV87jvd55sa9fM0F2KK1u3SbsDyCCbgswAAABAAnwF8CfbG9fMGd5FiutbtzNSaL5WqyXdhA6qtQfZwHlldXZ2Jo7H5NLZ2d1v3T07+97N+zdrUnP5rh/+8IdnN87+5tHfvPK9V27ev3np7ExE7tstP3BVETG3++jRI/Of9+/3NuOHl/7j2bdW/+afXnnlTBcRqd23PwicV+jHPFV8nO2hnFdd27X9Ptq+GOLX88svvxSRJ5980t/aor5QWNd/6ezs0bcenZ2dmV/2S5f6XR9c+eabb8TvCRARFdqA7OG8Qjw8B/AnG9q1tcwm3guFQtJNaKvVakq1B9nAedXl4cOH4vzdz+cvyIV8Pl/IFyyv5Qe8SySfz1+40HpjPi8iebvlBzcgGuZ2P71wQVp7lM/bNiMvF4xdERExj4O15ZxX6Ofo6CifzxvTFng928M6r7q3a/t9tHsxxK9nwFVFfaHoWL/NRc/h+jBY4bErFfn8VuHWv3v470SBey2uV4iC+/Pq4OAg6sZk1MnG/GQzhVxeGimLrGyd64utP25vvPXmWrui+vob180Es9MbT7Y33npzs1w1o9viyrLlrUryOgZ++62OwQPV8tLkiAdqFQAAgJgwwhYAACAKHSXVRUSq5bWlycEV61rvs0a31fLa0qTaMav/MfAAAAAAgFQ7OjqKc3NGnyzvJlZ3z1e3tZGlcmfufWN+qVwVKa6s376+OjEhZq228tL89PHu6kSfN25rxvuso8FPtrXJpbKU39y4vqhsZXavAfzk9MrKiv/NTU/6fy8AAEixqampo6Ojo6Mjv3dvSITRdYhJNwCo6OQXm1URWdnaNSNzmVjUd7dkZKlcXXtre7X9eoft98oiUlzvqOU2sXh9vVheq1YPj0WyEsBPrDJtEgAASCljJAs3MwDQkuaHqq34/aXuKH3xpRUpl2X/kxOxH9C+qJ+fmz8FJycnx8e/+OS9w82y+nPNBZsHHgAQjPGrGXPvNX900UVEE0byY1iE9fU01hDkFjnSC0Xw5gFAQo4PqyJStOnmvfjSiohUD48d3n2yrc2PjIyMjExOTi4tra2lIHoXxsADgLOKVKQVuxo0TRMRPVMZvM9FhC6yAAAghWaf8dHd3VKcXqRYLM7OLk+/9Pzzn1xrv6omMvAA0J+micgdvZR0OwAgJr1PLYPSD0XkqrYZ2goBoMP+Jyde33KycW3NGDx/fH5+fr67u6vrq2pPH9dCAA8AAAAASJ3J6aLYd5RvFqnrV0O92fd+/XpXzG68rjQCeAAAEAkGV8MqRSU/AKTExPPLRREpv9c9c7sRv3vuXN98m5+cfmwI4AEMr0ijC2OQfKVSiWLl8TJ60nbvCPfiyBSjqoU29DUaGTcEQH2W8LoVwS/NaxsnxqsnJxva/JIxSdz1xT5vbKbu197abr1ysr0xP7JUjrzxgRHAAwAAAACU14q7J0dGRuY3TkRkYnV3a6UoUi2vTU42S8qvlasixZWt3dWJfm+cWH1jRUSkvGS8aWRkcmmtKsX1rfV+nfJVQQAPAEPH7HpQ0aZFpKQvJ90iILMYRwAAoZlYvb2+Uux6cVHfPd6yvlxcWd863tUXHd+4qHe8qVhc2To+311dfGZWROk+9EwjBwAAAABIgYlVfXe1Z5qMicVVfbH3Zec32r9pUT8/123/oQgy8ADQV8BJ4Et3SiKiiSaS8hG2xoDYlDYeKom/dAIJ8GCMb71qt68AMLwI4AEAAAAASAECeABIWIrKuRudEZp9CgAAABAvAngA6IPplAAAAKASitgBAICh46mehW8+RuBvvnhRRJbf/yqqNvmitUpgRH3EonZ0dHR2dvbw4UPKIgBIKTLwAAAgfKmoHqepWprRCOOtUjTWpldJLotio29SeiQBgAAeAAAAQ4cYHkAaEcADQFSMvqaVSiXphjj7XEQGTRNl/FXxHQEAV8yOIcTwAFKHAB4A7PVOAg8A7vkJDlXt0m9SdtCBV4oP7gCAfgjgAcCteKpeAUiG8dXuDFCDjzxXPFBMRamCiKS6rACAoUUADwAAhoMRnPMMDp2I4QGkCAE8AMAn8lfwgdPGQUWtShNGZ4TsPO8wzronn3zSfIXB8ABShwAeAAAAQ4oYHkC6EMADGFIDRn5qmojc0UthbtJuhG3qGFX9lJrPGVCQvwp2n3/xeQRt8dCAilTu6CXn4p2ZqWNnIoYHkCIE8ACQPDoVI2OGuTSalY8j8Gd/cjmKloTCfwlP/VBErmqbYbYmVMTwANKCAB4AbIQ1h1zpTklUzVdXtGkRKenLrpY2OiNkLvMGZC+fHLohmYCDGB5AKhDAAwAAAMTwAFKAAB4AACBM/gbAR9CQqGS42wLjPgAojgAeAFzx14nUWL5SUWpqKH+MHc/AjgAx8RoKVqTyZ39y2Rh3o6yAHekTrPfhvi4DRUkAqIwAHgAAIAUILGPGoQagIAJ4AAAAoI3B8ACURQAPAAAQmuAD4Jff/0pENl+8GFaTopDhYfAGYngAaiKAB4Dh0jkQ9HMRkcCz5QGeJN4VvG9JC+OVMEJTH7XQtKti36pkGAfBpjHKtDByxPAAFEQADwDdWhOkHybdECCV3FcLw/AoyWUR0SRleXtieACqIYAHAHijiy4pvBEHVFaRiogoXoI+vYI8VCKGB6AUAngAAACgL2J4AOoggAeAGPUfYZv4qGB/jGZ/+eWXSTcE8MMYzu1cj83TdzN4Bbs4uclL960XMAR17KyI4QEoggAeAAZzuIUFoKykRuP72OIdPU2d54fzYkhZBwAqIIAHAAAABktpVykAWUIADwDRKpVKkpG+pkaGMAM7gqFkfAeHMnWM0BHDA0gKATwAAEAI0jUAPhSZeDTpDYPhASSLAB7AMHIYGWvMjlaS0MajGpOuVa5WwlphYpoV+NK/I0CUfIyU1nSpVCqlO6Ukx5ZrmngZip+iYfChl0IghgeQIAJ4AAAAwANieABJIYAHgA5XjQxzepJL/lS0aREp6ctJNwRZ4zLbSTEwfyI9bhWpSLPTkNExPuOXwYCI4QEkggAeAAAgqCEcAG8YwmHwJmJ4APEjgAeAAZgEvpcxsF+jIj2yp1nrwee57WegdWqvLd6uivqhiFzVNqNqTUKI4QHEjAAeAAAA8CnE8ngAMBABPACoglHBQPyMNHKC/cCbM1/c6Sj/vvz+VyKy+eLFZNrUkoH+R6GXoLfF1RtAbAjgAaAt9Dnk1Pa5iFCnCgDCQgwPIGoE8AAQr2AjbAEEFEVWeWgr2BmGuY6dicHwAOJBAA8AkSuVSpKRe1yjb0IGdgQIn+8KdpVKRdLZUz2NbY4OMTyAGBDAA0AotGyEtZqmZeJBAwAkgBg+UkdHRxxYgAAeAEKUkdDXUwxP9SaY4ikYhpQqyWUR+fOpP5dMXzGI4aPGgcWQI4AHgLarWkUkxdMyA4jfkA+AN9Bzx4oYHkB0COABwEkGZlEC1JSivhtumupvALztHHLx6N9dwgjF3V70VL48JtglhBgeQEQI4AEAAICQMZYEQBQI4AGgKbpJ4HXRRaRytRL6mr0Kmo9qzoFXkdZOaVkZ9g8AoUtRNxMAaUEADwDBEcQCiAkxYRrxeQEICwE8gKET2ahIYyAowTyQMsYobn9l2IJXsFNiEnhNE5E7eqnzNW8VQKhjZ4vB8ADCRQAPAAohtwZEwogtXcaizaEinidT9Ebh2m9eqVzHTgXE8ABCRAAPAEOnok2LSElfTrohABCVBEvQ9yKGDxeHEcOMAB4A4lAqlSQjXUyNTrYZ2BGETKl4KRUSnEMO8SOGBxAKAngACMjbnMkifjroxsM65NXTs4Ynn3xSuCvF8Ak+AD5jMvGMMkLE8ACCI4AHgKarWkWkY2Cq1xpOqfK5iHh77gDATkQD4Jff/0pENl+86KNJPlSkPT2kDxm9ToaPGN43+vgABgJ4ABCJchJ4AIngdh9q4pwEEAQBPACEiJnkAA+YdkE93scEwTvOfAC+EcADAIBhEeK4GAbA23IaBq8fishVbTO+1iiPGB6AVwTwAACfjOGyGj0OMMR8D4BvfnGa89NnIeOt1F6oP4CCwfAA/CGAB4Ag6G4KAPCDGB6ADwTwAABg2PmYPTHbMj0Hh0KI4X2gggCGHAE8AAAA4lCSy9Kasg4GYngAnhDAA0C4c8jZF6I3hotXrg6+bVU9t2Bk5DTuv9FB/SHHAXV9MZOtYBf8KhHp50VHBq+I4QG4RwAPACIiV42I1NJfNPQepKVSSTJya2s86cjAjgB9NB9UDTjJfVewyyq63PtGDA/AJQJ4APAtUxXsrA8sfIwHVr3jAACojRgegBsE8AAAALEyhu3oWXn8p5r0DuhIY5sBxIwAHgCS4K6DLgAFJTsAPhWc++/cnbor5Jn7oDeTrfQ+lAFCRwAPYLhwEwAoJb3hSvAB8KpM1aZpInJHL0mzOHyg9iS/O1mRxi8FgBgQwAMAAACqYDA8AAcE8AAQOvuZ5BTxRWVT5POSvhxK+T1jEO+NSzeCrwoAYCCGd5benjtAcATwAIad30ngM1WCHsgyYzx2SF27GQDvUiamzEwSMTwAWwTwAGBDlbGpoTLuAh+7+5dJNwRZk43SEp5mTwwyAN59Cfrl978Skc0XL3reVjABL4DJXjmzcTYaiOEB9Ho86QYE9/HbL9zcsXl94cYHrz8be2sAAACAUExNTRnR+9HRUTaeSgAIKP0Z+M9+eZp0EwCk3VWtIhJaD9vgGN0HIE4VqYiI7nkkEeJAHh6AVfoz8J/+siak2wGkQ6lUkjuiaVr6O+cbN/oaVQAAIGpmHh4AUp+B/7i6IyKFp7+bdEMADJWBFeyULkTfq3fIq6fxwAY6DkBxydS2GOJabtSxCxEXWACGtAfwRgf6Qun3ryTdEgAAkCFGnG8XggapYGcK+DRB8XAu/Z2M1KXshw4gHmkP4D/9ZU2kUHr605+9vfqCYXX17Y8/S7pdAAAAQHiGdjB8lmYWAIJLeQBvJOBr79y8+c5OrflarbZz8wcvvP1xgs0CkBp+J4H3w5g1qnK1EsO2otXMTKZ/R6AGxZPJ4XI/h1zsjG90xA3TD0XkqrYZ0eozH+kNbQzfa6iuG4BVyovYGRXsRAoLN/709WeviMhnn/1s8z+8s1Pbubn69E82XrbrWf/hhx/2W1+tVuv3p0So1h5kw5CfV2dnZ2I5CGeXzkTk7ExE5L7lyHQt1uXSpTMRuX+//df/euOGiPzezZsOyxibe/To0dnZWa1Wu3R21rVRh3aGwljno0ePfuu3vjk7O7O2zXZzDm24dHb2rW89+qezs/v3a8YxNBaLotlQ38DP/dKNG8Z/3Ld8R9ysIci5ZK7T/K4NbGfXAl1fUuOvnlrV3vHOa07t/uDGGL755pt+iwX5up2dnT355JPGey+dnT361qNHj+Tsn5qXhVC+yP1WYuxRRNcKl6tN9TXqX/yLf/Hll1+KyN///d8/+eSTSTcnDv0+VtV+cdRpCbIt3QF8cwq5hRsbZgn6K1defn3jaXnh5k6t8nefvWwXwf/hH/6h7do+/PDDQqEQUVN9qNVqSrUH2cB59fDhQxExD0Je8iKSz4uI5FsvapqWz+cdx3Aa72ofyeN8XkSOb916rv2u7mWMzV24cCGfzxcKBXn3XdG0/K1bttPXdbUzFMY6f3Phgshj+Xze2rZ8Pt+7OdsXzb+JXLiQz+fzhXfl3VfOXrlVuKWLHkWzobijo6N8Pj8g52l8xyzfsi62Z07A61V7nfm8sWmnU7rZzM4FWm80/uWnMcaO63refEHyIlLID26M4e5jj/VbzPfXzfjIrLt5QS5cuCD5C83Lgsu2OXv33Xc1Tbt161bXtbTw2JWKfP4P+X8oSSn0a4WbY5KB38FCoWBknh8+fJjh7gamfh+rUr847s+rg4ODqBuDbEt3F/orL2988MEHvRPIPVtcEJFa5e8YCw8gAgNL0AOA0oYh6ss2+tIDQyvdAXxf3326ICK1X36adEMADLGUzSQHZIm/sdA+Zk9MnYHxXjKT7cE7nsIAwymjAbyB2eEBuHDVKMYW4G71o0zf7gNhGqovi6o7S+CXGRRyA4ZQqgP4z362+sILL9gVnDeK240/zezwAKyUK1Dcf6LpKH1ubDvejQLDyu7hoEJZbk0TkTt6HDNxRE25K3yMiOGB4ZHqAP7K0+MiIjvVrgj+s5+9uyMiC8XusfEAYBXnHHKmUqkkLvropiGvYhw3RXOMgIISn0NO7UsK/GAwPDBsUh3Ay7PLrxZEZOfm6tsfN+vVffbx26s/eKcmUnh1mfgdQPj6VrAz6s8r3p3+i8qmiJT0ZeuL/ZKBPsYDp+G5AzDUhjNBnW1DG8Pzi4PhlO4AXq68/KevFkSktnPzBy+88MILL7zwg5s7NRFZuPGntnPAAwAApMby+1+JyOaLF4Ovijgnw4Y2hgeGUMoDeGMmuZ/cWGjPu1goLNz4yQevP0v4DiAWiqfcAeUMyVdGyd0k/Z5hxPDAkEh9AC8icuXZ1zc2Pmja2CB4BxCAv+JSz9kv72ImuWTq2AExMk5vXXeY60HRrrDBv54qlKkTkcHHtiIicda2vDt1VxT8xFMukzH8MNcmBGxlIoAHACTq5v2b0irQhaGSgntr8/FB5nh96qH0x4SQ8CkDmUcADwAAELkES9AnlY/1UQjTtxQ8S4qLov1ZAISEAB4A3OsuQc8AeMCPJL44sU69rt6Vgch2CBHDA5lEAA9gSDWzYc2QPNA9vTkAfuBMckbyrXK1EmRzSmiODU7/jiBmyndlDzNp3GdnY32U4EJFKiKiS0nUaxv8yeRgeAAGAngASBp17ABEhhBuOBHDA1lFAA8AkXJRiD4Wxj3cY3f/MumGAIgb/eeH0zDE8Az4xxAigAeAuJVK7a6qDtJwX1ISERUeTwCwpfYFBJEbhhgeGDYE8ADQ5jj+M5sV7JyHvPoYD5yG5w5QQCa+PvYU27VhSL9Tgt4BhwXIGAJ4AAjkuVCqPTEMHlnVOw37kBRI69zNROaQ4zkaDDxXBbKEAB4AwjSwED2QGaQ91afip6MfluTyVW3TqH6POBHDAxlAAA8AQ+VzEZF404CAGyQJw+XlSHJZyD4GwwOZQQAPYBgF785Kjr2LcTA1CtrBvUx+iVTaKRXT70hOGmN4uvkAvQjgAQyLKO4D3A2AV2UmOUAVKRoG768+Rf8ddC4bGRa3EZqmicgd/XKkjYE60hjDA+hCAA9giPWW1+q/qIhE28WUOnYA7Cy//5WIbL540dO7FEla+pjJApEihgfSjgAeAIbFF5VNESnpy0k3BFBa6DFndCXowy0cEE/vgNDRy9qrjMXwlM/AsCGABwDPGAAPhCNjXyU1dodIBgPxvANILwJ4AGjymn3qNwCemeSAwdKW5vVAgV0jPMNAJK6BlCKAB4AEZGhcqC4ily7dSLoZUJKHMhMIB/EYvOKcAdKFAB7A0PE+HjV4Bbt2IXqn6dZ66thFnSFx0+mg77OG/lX3SOwMAwYeK4sPBS5lbDA8MCQI4AEAQMaltDybV4Rh8IoYHkgdAngA8IbB7UCYIvhCtXsHxNmHX40rw7Cl3+kJEhwxPJAuBPAA4Ee/CnYA3Or5EmVh8EXPTlnH7ETaEcDvcauIiEgpzKYgbYjhgRQhgAeA8PkvRN9/YLn6nIb3A4heuhLRd6fuChGjMhSM4eleAdgigAcwrKiPDWDoVeRz8VbUE5mV3lA5C513ANcI4AHAWUcJegbAA+HLwNcq6V1QOXTJ0KyZ2UckDKiPAB4ARDyOTfU1AL49k1xyHohIsPnwgFCp2gWmI+Z0P7Al0d0JmD4dkkL9cIkYHlDW40k3AABiZSkoRToIaBrOm3Xneg2VqxVzmatSEZE7/ZcfuACQFlNTU8YF4ejoKL2d6oEMIwMPIE2Ojo6yH2mkuY4d0sj3d2rAzb0lfa2Jplp1Q02mRSqtGuzR8jTC/CNt+iNt2usmUhdoleSyiFSCHX8fRc42X7z48R//Ky6wzlJ3OgFDhQw8gPRJKi0Q7gB4Xdc1TdM0Lf19VnWRV0Q0OuenWgzfKU001Yql6VLqd95qdzQR0UvtwS8lp8YPXMCbj7Tp5/RDN0sSa/mw+cXmsqbuCA4VGHl4kvCAgsjAAxgKfmej6ahgZ3A5AN7/THIRMHb/sbs/tb7ofsirjxpUVEKCN2p8U3xy1/ggg8yX3/9KRDZfvOjjvQ4NEpE7+uUw1+mSfigiV7XNBDZtleqzDsCwIoAHkErDExkSCSPjMpMFzcyOZJc5juPP/uTy5hebIsTwANKHAB5A+tCjz4cvKpsiUtKXk24IoBqFQjgfo9/h3ne+X1YAACAASURBVB+8uCkiY//P2GHpsB3DIxN42I3hQQAPIK1S+DvdPZNc367p1LHDsOIuPF5GDblSwq2IV+Gjj0TEiOFFuNICSBkCeACpFDQJb53e2R1FRrMrzihRplq9caRDSr9iKW02RESEjvQAUocAHkBq+C1E12aZBL7zddfFpVxWsHOWnSiXngLpF/xrFYIMjB4PeReMlDjfLCdeT93eS247CS9cx2wk2x1GiUsToCQCeAApFvGNhU0JegAZoMm0iOjiap42ZMnl4+6q+9NXW3PRE8MDSAMCeABppf6D+UAzyaUquX3//k0RIWGItj6jVFTteGLTKh+zJwaUYAW7IFPcuRH/wexlVLDrclg6FGJ4AKlCAA8g3eLp3RfdAHgV7muBSPUbuhJfA5yiU+W62DynH7ae/VUGLgyXzCdHpcPubhdGDN9+2MTVGIDaCOABpFjMSfgwBsB3F6IH0KZs7NSvR4zrBked4oYbvf3nTdOVaWJ4AKlAAA8g9ZhxCki9KCJb75NN+ERYnnLNJLyQhweQAgTwANItsiR89irYPRARc4+85gN9dPVnQm+kSsIBW4ID4NPIU4lyTTTbAfBdpivTIjyOSTF+dDAkCOABpIPz7ZqbH+yKVCTRgbj99I2NW712uSlBRJioSUSUek73nG6O0GYmuZBdPr7cOwDe1KxmZ43hScITEgNKIoAHkHqew4/OjrUDc9FBKtgFKkQPDKHovyyhzSGX/u91RT4XJR9rJqLdkd6U/o8YQPYQwAPIiKhTBGFUsAPQX+srlrKkX4qvDEaV+1LCrYiY15kLuzvSE8MDUAwBPIAsGPo+wAoxsnmqTvcNOEjspGUAfKT+4MVNh/rzVh1JeGJ4AEoigAeAXpFWsDNWm70ZnpkhDy19yr8r/HBHiSy6ZQA8wucwAL5LMwkvxPCJoTwH4IAAHkB2RNHnNp7h6wPr2MXQBiAKRrjubZR1Kk74iBvZKp/RftK3/P5XIrL54sVItxunkly+O3W3IhWlhkt0VLMTYngAyiGAB5ARDo/qjbvDu1N3fa+cAfDIHhVzXGF80ULcr8GzJ7prsNdZG+OgaSJyR78sajbPkfuP2F93j+5qdsMdw6esJgUwBAjgAaSA+9s1bjIAZEWmZpIb/DQkMi4HwHdpJ+Fl2GN4AEohgAeQHQ4RfoKTwNvOJKdLSWIcDPxFZVNESvpy+KvWdRG5dONG+GsGEpBAeEYFu0j9wYubPt5lM6UcMXwa0F8Aw4AAHkDWhPjLrcT87cZdYyV7Re+ABPSfBD7hDuRDV8FOPxSRq5qf6Nol6xNS9xXsrDqS8JLqKQMBZAcBPIBM8Trw1W7wZ3cJ+ggGwNsUoo+zf6m/Ia8+WkgyBD4p+MTKWlFShUd7cMdf/3nprWZnoLAogKQRwAPIIKeIsc8EVwCUkKLvZoqaCl9sOtKbiOEBJIQAHkDWqFVVOyRTd++K31S28a7H7v405DYB2RRrYMYAeK9c1jQ1+s/7GwDfpW9HemJ4AEkggAeQTXTbToJxX8tNbQokNYecUUhyYPlG44nVl19+GXyLXgaMJJZRT2IAvDFIoRT7dmNl9J/3NwDeYJ+EH7IYnsFQgFII4AGozkekEUpYEmIFO9tC9L36DjJPWx07l0EasskySsU4B3zO/qDsCR9XzPacrouUPtIqPBRLXHcSXoYuhgegDgJ4AJnlK10QQwW7RDwQkcTrbAOupOJL57qR/mpGIohwnx7aV7MTYngAySCAB5BN1iR8gpPAA1BH/znkehaMBQPgI3V1OrROHH2r2RHDRyCpAT5AWhDAA0AibGaSA5C07sd8MczvmOwM8JVKRTLdQSDIAPguNkl4IYZXDiP2kXkE8ACyzPdPeIgD4AEkgAkj1RPD05DoOE0px2kGIEYE8AAya/A8Q4PGpsY/AD4zdeygsrT0UP3W3btJNwFqcXPqRlo+0z4JL61LdDqfTbhBWhtQBwE8gIzjhgNIH8VTmoo3D9EUPelbzc4quzE8AEUQwANQWsBUYfcbB/Sq7S5BHyKXM8nFIEhNbBc9YLungidvgy4pmWUw8ua5rmBnTNWuicjy+1+JyOaLF0NpQEU+F5HSnWxOBV+ZjqRAoKuO9Apc5wFk2ONJNwAAlBNnmN0bxlSuVmxff0W++5RcjqlZwPDS45x3PdkKdtnTdeUMsYKd1XRl2j6S1/Vm9K5pdNMAEBEy8Or68ssvk24CkAVGBvi1qde8vjGuAfAehrV/IZ9H1w7DR5qmQjcBpJWlk4sRSgXtyRze2eh6DrnwKVfjXZsWkTv6svt3RHRlKMllac30GaJIJw11SsJLhvLwmqbiLqjZKiBeBPBKo9MpEJwm2t0pFUth6XKoS0mXki669f8OS4elOyXRpOv177/2IxH527/4cQxt83annvXqTUjE1GuvicjR976XdEMszK+jawErrreG3qhSvZKneyankfAZiOHNlqu5C2q2CogLAby6nnzyyaSbAGREpNmYMLi9FzFieK++qGyKSMlLti24lIxzhtL+h4SnXQjn7LVNv7seAI+2gSVRzAtORAPgTQOS8JLyGD6Nbe5E7RVkGwG86rj6AEF4CSAjrGDXn5/NxZOER1alZQ45UxK/g+FcBwam39M4AF79JLz5xDaiAfCmAeXoUxrDm63tefCkVlScrqMKhIoAXmkpusEClOU1/a747am/JDyQUlN370rCefhAlBv9LuKjZ4F5VYyrMkgKuJpSLr2HS/GWK948IGIE8CmgysNOIHYBU4XO6Xfn2dSiuE91nEmu48WBs7WRhMcwuH/zpvEfyf4Oupg90UbA0e+d2jPJhbvOSqUirp8ymFfFcJ9yGjVKgn/E8Q/YGdyRXtJWImTATKtx8PC7n5ajCoSNAF51JOGBIJQf/S4+OuuShMdQmbqrYhFKN5RMvweleBI+/mv+gCS8Sf1oU/0WWql9HgKRIoAHAEVEd/P0QETcPCkwEmuOPQXsNTOlmrWOQKruBRGv2GocOne0cbOCEBtjSriCnVHTPqH4p6M7g34oIle1zXA3EXUFOysPSXhRO0K2HfquWvcBBToIACoggE8HetEDXtnEBi5++xMaAE8pO2CAgL+D3ieBDxQkuOk/n8YKdibFa4VI9BXsrAYn4RWP4fsXrlOdmscTiBgBfArQix7wx0tfyo4S9Mn1EY12PjkJISHpZzywWrWLh1u0Jegjy49N/fmfh77OqKnaf95/BTtTIldI51M3wRkrXVWzE4VjeC/Ru1oXc0W/YkDkCOBTQ5XLJZAGKZx+vPtGxE2cTBIeyjK+gyEMSLbco7v5HVRhkrxQy9eZQ1rCLcVf8tcMKwWT8EkVPXHVkV6UjOHTm3s3qXMwgbgQwKcDSXjAKzXL1/kYXt4PpewwVJL7HfTzbXVIvyc8AD4kypayi3MAfBdX1eyUiuEzEL33b7lanQWAUBHApwmXIQwV35m0FKbfrbw1niQ8hkq8v4N+ohqX6XfvA+CjmEkuKEWS8L3X/DgHwBvcJuFFmRg+A9G7SY3zEIgNAXxqkIQH3BuYfrcdCp70zWgk88kZAc9jd3/qp0XBxFZpHENB09LyO6jq6HcJZQC8SbUkvAq9rtxOKafOoVOnJb5lYBcAjwjgU4YkPODMV7ioSAU7U3MXXJaLCysJb51Dzk9Xf9UmHEJmdH4lVf4dDHf0u63l978Skc0XL/p8v1YRKXmNeRyuikk/91SI22p2hmQvmO7rTapzYXduswotBOJCAJ8maUk+AMlSIQ/jVyRJeCAz3P4Odt7re59Drnt17hd1Tr9nYwC8KbbHnQ4jqqwPbRMcAG/w0JHeFH/kmb1YN/nH7kCsCODTR+XkA5CsDPXWTvtIeON2KjMfR0aoUKHdFGSERYy/g83AwE13GPfpd0VmgK9UKhJGh38VkvDW57bxD4Dv4rkjfZwHMPDQ90iLwwW6RilwHgLxIIBPGUVuvABl+U6/q3APKiIk4YG+NE3U/h1UePS7hDsA3qTAmCO1eEvCxxzDZ6lwXZfs7RHQ3+NJN0AttVot6SZ0sG3P2dmZau1EuqTl/Dk7OxMvrb1x6YaI1O7bL3/pxg0RuX/zptRqXSu/dOlMRM7ORER+7+bNqI/P5A9/+F9v3Pi/Xnnl927etG/qpTMRuX+/u50Ovvnmm37LnJ2dnZ+3F3BYYdef3C9pqNVql87OROS+9agaf7pf8/ppIgpRfwrWE+Ds0pn0/z422+NimWZrf/jD5le4dRo7nPDm/96vNU+8bz16JCK1T/3suPl9dD56N27ccPir6ZHREo8fQWunxGjJN99842Mlxo7I2b8SObtfq924dOPRo0ff+4vv1b7bdz3GdgdeFb3elliP5HcfPfrmt7559OjR2T85raTfwTcu+9I6i9wf3kivRY8ePZr8m8mPCh+5Wrp1bssrr9zv86MQiuZWLL+Dbt9o+V4boruSOKy5txm9C3T9Nf7fHX7jEA8C+A6FQiHpJrTVajXb9jx8+PDhw4cqpyCgsn7nlWqOjo7y+byn8zwveV10yff7c15E8q19z+fz0v7K51t/j+kicNyx9V5Ge26J6O+++66mabdu3XLO7P33xx777399xzYV//Dhw8aIPPbYY8bm8v033dUqh0b2rqR5XnUc5Hzz/0UK+cLDhw8ddxlxiPZT0DTJ50XX8yKaaAO+jyIikpe8iBTyfdvTcb2ynF0Ov4PGPuZbCxv/IRcuiP8dN76PBYfvjrFFN+n3Y18tKbz77kea9nd/Is/pks/fuvvYYz5W0vw6Gv9TKOQlf+HChXw+77CeQVeq9mLHt265T8V3XNYuXPi+XDm+cOEfLvzDa4XX+r2l36lrnELmmfapu8Mb9e/gceF4ujL9/KfPu83Gv/uukRvP37oVVSbZ+HqKGN9Qbzp/PSXKK4nTmnua0btA1wGM+XfH/Xl1cHAQdWOQbXShTx9CdyBUHSXo1RBmL/ovKpsiUtKXpc/keQZrCXqDQyH6vuOB+9crjnTYJIZNEr+DIfRwTr6CnVYRCb+zsSK96BOvYGfluZpdpH3pA/acV6cQvbOeveN3B1lFAJ9WXI8Aq4Dl65QZAN8l7aXsgAjF9Ts4OObxNHucIhXsQqfIVTTxCnZWbqvZGSKK4bMx7t39vHdqnIdApAjgU4kkPNDLffm6foloRfJIIhJqEv6BvxUiY5QqQe9f6+5ctR1Ru3ydRFTBzhTpxbPfqav4tCN+ppQLPYaPJnpXN7Ot+tcQCAcBfIqpeOkEkqD4bVwwJOEBEbG/NXfzO/jF/7EpgSaBH8BT+j1pJa9v8BSZJ5KE9z3zSDy8JeElmhB02MLaNH0lAT8I4NNKteQDkCzF7+G6OAwvt/AwAbVBsfnkdGlPB8/tFGwEmQpeYv8dvHq10u9PbtLvyQ+Aj1iyPZiUGgBvMpLwPmP44FGo+27nWTJs+4uhRACfbiThgWDBoYIV7HqRhAf6cvgdNB57PfXU5cAb6XuJ8Jp+z+oAeFNsSXjbK79SA+ANfjrSm4IczCHPQg/57iPrCOBTjCQ8ssrrYN2A6XdFai/ZCbMcPZARli+sCr+D8Y5+10Xkd/7lA4/vinYAvCn+JHxa+l7570jv7+cpG4XrfBvOvcYwIYBPPZLwGGYh9s1WqYJdUF6T8L1zyBncdfXvlJYJh5BGfb6k8fwOlkol6Uy5p2r0u9nfqHkMK5WKDHr64O+qqPAj0WT4TML7juGjiN7TeGFPV2sBLwjg002F5AOQLFcZmJQPBWzdO7m6HSEJjwRYvmLGY7XYUqNx/g72DoN3mX4PPgDez9O0JHiO+fVDEZn68+vi5UFM16NbNQfAd/GchBdfMXyac+99O9/5+AVP4e4D7hHAZwFJeAyn4SiN5vMuhJHwsMrIHHL9Rf872P1N9JF+z/wAeFOIDxocTt2uh0QKDoA3+axmJx5j+Bijd3VnkrPStHS0E/CIAD71MnxDBgwUPMv3kda3srRSHCpg9yIJj4zrDGYcfgcjnUMuqbnf/81//l9FZPPFi+4Wj2kAvMlNEt795BqZ4b+ancsYPs2590hwHJBdBPAAhotxy6jreuu+tiRJDID30iE2nLZZdjwEPu6/yYQgBNyU++T5uGWpLIg6/CThxUUMT/QODBMC+IzgnhjDZjj6z3fwlIQXetFjyMT5OzhUqWN/ohuun96Lf6Ap5dxE5kTvvfiqIosI4LOAXvTIEveDddMygVAY9N4K2M7c96LvV4LeEKwQvbHOdAxSQCKMb3HwkCy230HzOZr7zizBK9h18jWTXLyFPGPI3pvX/1RUsLPymYSX/qXg4/lw01iInicayCgC+OwgCY/hkd4MTMxIwmOoRPw72AwG/KXfh6eCnSnOmvkqV7Cz8l/Nzsp6YNMVUSeiwkNkZA0BfEaQhMewCSX9npYKdqY0lrLTpSQ8c0lUNkvQ98Qt7R007tejTL4lVb7Ou7gr2JlCScL3nroZuJKE05He+JgSHfoeU1mTgP0LUvNVBTx4POkGoK9LN25IPu/hDd/73lH2btGgAE8/zzGcgRVt+qqIjxvTTklWsPNFFzGSNp52/HcjaUvsfNwjcjGMnvVUrFheqfT81RDSF03Xk806Xr1aCXz9iZnutcFhXRU/0rQoLrAZGD81XZn2Gcmb5/+wFq7z+hDnFfnuU5WK8IuADCEDnx1Td+9KpXL0F3+RdEMCOWpJuiFo+tsf//jBX/2nB3/1n1wuH/Vn91f/9rcfPPhvJSl5fmf0tzg///nPf/7zn3t9V6S9TL//2q9F5Puv/fvoNhGD7f/zf/t/XZ+BJi4jHmhaoJDY6MmiG10tKtLqdtG7nMdGaTdu3PAx08HU3bvGPyOaQ84oSBEdf1cST4wu3IFSwaEqyWURqXivl5G6AfCGoEfe+nMW4U+b1vo/hfjogvHua3859dprUTQGSAoZeHXdv3kzXyh4e0+ao/euu+1s9vlMlb/98Y9F5F/+zgMRufzXP33qj10Fgcbzl6g/uMrVSkmZW0+Djxvu53TdU/ReKh1qmnbnjrfuu9/Pyn3L//2n/8vy+1+5Xz6eUzFTNM1LMGCcurrjf/e+RQstD99HpJ+4j8vOR5omUvKRhf75z3/+R3/0R9ZXjCvGR5r2XEoSrpYGhzQXZp/gLS0D4Lv4T8JLDI+k1Yrbu3jsghHHlQeIExn4DEpjEt4avVtvv8ihJaUreheRL1686OaNKYqX1BkA7z6MT8/I29D4vgik6FRMLa9nY9hnb0rKdyU3SCexAfCRykD/eVGp+4OdFJwGwDAjgM+UNPYRsnaYn5qaanaAbP2HEMPH7m9//GMjeheRf/3Hv/7Xf/zvn/KS9jT4/tTc97yo+Kria3THtYbBodxb++7v6m/rIc5B7TyHnCHYTHLhOPvrn4rIprunSFZcQLxx+5EF+WTDOCtS8iQrsnjY00xyutcyYCkpC5IFQcvRR8I8aXtOgyhnkqPfJeAeATyS1C/x3vUKo+JjY4buIvL9H33qbyUx/ACr1n9eRMzOrlEPXh3CJLzBU/95A/eCrvgsheXvPHT7LutTqhCfWMXPazAc9QVEFA0aB+vtP5/SAfCmcKaUi4r1vE3xFzDljQfsEcBnUyp60fcm3nvRnT5OZvT+/R/9qBW9d9x6uuxFb4ji8/qrf/vbIvJv/vM/G//0l4QXEWsJ+uDMG+6uAaueeM3UeQ1perseBGesbVBLjC02Ryt4nXbo6OjIKF9nRu8k4b1ym9fqmp7KSfAb4o41GD2ibYc337x50/82NE1Ennrqsu81BBQk/W5cTCIN5uPpwu3Qf2fgBcT21O3tP5/SAfAG9TrSd9WwGPCTEflMcp2dR4yrhK8xFEP64BtZRQCfNanoRd/Vbd55YbrTx8Dabf77PzInD2//4HntRa9+Ej7cAfBBQncfvVWHLQlv9J8XkvAxcHtqBTkDh+jspS863FAmCU+yGkgHAvjMUjYJ79xtvh+600ens9u8Eb33/RX3lIRXn1K316koHxUnvukYjG9NX9msYJc9yiTh+w99B6AYAvgMUjkJ76bbfD90p49CZ7d5a/Te/RMeZyk79wL0og9HKH1cEy9lpzIz/W6iF33knE6tsM66wOsZ9K2JaBJ4l5QJhj1fW5R6rGnyMft3uiSdhCd6B9KEAB4x8dRtvp+u7vTclAfRp9u8wxzO3qjfiz4svf3nfUf1Mc8n56YEvSFgIXpdShLSLTi96EPW+5kOPh+Cn3sD1tD7cCqNj6v8zf0efjs6RxEnHSgGYh38nPYKdlZJV7NzE713lrpMzfcxLe0EvCGAzzJ1etH76zbfD6n44Oy6zZsG5bUiK2XnXG2rq4KdyWsS/upVY/R7CBXseu+2fQ+GJwnfxTgZuirYmUjCh6z39LM/texetMSHHktMDTh7jadUaaz4EDD9blxGbOvYmY/SjCuhcVX0Ic5u234e/7mW6gp2Vsl1pB8YvavyBaSCHWAigM8mdXrR207zHhyV7Xw7fecdu27zhgE3WGkvZWeEu6VSKcQKdkHK1wWUxsDGq97+80IS3gufUys7nVphnXXhnb1KPsZKuiN66gfAW0/dzPefN8WehA+h53zkhegB9CCAz7hkk/DhJt570Z3eK8fEu4fO84nPJ9fF30j4gHfYEc3wFPV8cqng5pwhCR+57lMrijMt2DqVfIalUiSckgHw+qGITFb+24Clsp5HjT0Jr/y4987RHwBMBPCZlXgSPorEey+607vXP/FuGvwzmfYkfLgc0u8+wnvmk+vicPKQhA+HQ6hpf2qFe77Zr63fM6kUPavyFwZHOuu7IdUD4K2yNAC+S+yfkftzNTVfwJbUNRhwiwAe4QulXp17dKcfyFqvbvzVV+0WifZ3Lvjn0m8AvCnmcvQOt9rBO9VnLglv3CCGNmzBRBI+HA7RZvvUiu4ca49rtfaUtj6cStGDquDpd+sFxHYYfHDxD7d2Mwz+sbs/FbtvqEP/+cwMgDfFWM3OU8HayL+AAwf7MAAesCKAz76Ye9FH3W2+H7rT9+PYbd7gp/K8+170qiXhwxoAH8Xod5LwYrmGGPF5v2Q7SfjIdZ9aUZxpIa2zJzKsyKaIPPW/L4ezfi+SHv0uGRgA3yvz/edNsTxeUf3jBuCMAD7L4u9FH0+3+X7oTt/LRbd5g4d7Ix8TwosyI+HNEvRBbrJj6OkabhLe+GtXqO9+DjmDbQ7NWGffrdtNOOSy4pHLCwhJ+MhpWvS3+wHWn3y03KZYGGz59rk7SvE/ehhwARliUSbhAw99T9lMckAGEcAPhRiS8DF3m++H7vSmPtO89/L/GxxuEt5nueyWmEfCu0m/+4vzhzwJ7+lrSxI+EDf33+1TK7pzrGPNbh5Fqcx3DOzjcvGP/9+3RcT9NTyNA+Bt+89neAC8IcokfMDoXfUvoEWKmgp4RgCfcfEk4ZPqNt8P3elddJs3+Ok8L36T8Nng5j47wbnlskGFy0gGuHooNjjgDL94wUC9z6Q8PKXSNBG5o18Ot0kDhZJ+j+HSkdx84x50nbq2/eezNwC+SwQPXIJE705vUXgmuew83QasCOARSETTvAc3tN3puxLvjtG7Ib6fN98fxMAKdqaBvehr//Fzf22wiic+z1wpu6jQiz5yDmeWpYd2gCpTzttIjSi6oPfWsXNTEE5BA5tdkssiUknigZGCIqhmp/ykcV1Cu7YAGUQAPyyi6EWvWuK9yxB2p3edeDeEcP+XolJ2Rnz71FOXRYkqU04y2Ytel5I4FpSWzu+pcwU7E73oI6eXYthGCOtIW0AbmQxWsBtOofaVSFv0DsARAXz2RdSLXs3Ee6/h6U7vMfHus/O8KY2l7MwKdr5XHkP5OqvhTML7uJ6QhI9M64yK49Tym3pV4OmVejGw52Oi4GNN5+d9QyKMJDzRO5A1BPBDJKwkvCL16tzLfHd61/XqTEGjd6sUJeFD4an/vO+AP+okvNcS9AY/fXeb9YqNfzhFaL6/myTh/fj/27vbEDuu84Djj9W8ijqxG1dZdS3jS6q4yCmh0LDY6U2yFOyUSjYUVRDHXgyxuVH9QQiSULiEBMoW6oZsVUjlmzrFrN0YhNoSRQQUCBtyW6lbQ1MVR9RVnStbXrwxTgh22FiWs9sPs3s1O6/nzJxz5py5/x+0Wa9m5+XemTPzzPOcczS+xIGTCHkwkO5wOBzeXxLGK76lWl19WdxWpvg4sUWsCDnEEewiicLp1o9gF2ciCW88eo/Na+Dde6sxb3cMMIMAfiIYTMJ7Xjafp8Xl9Jpl82MG7uUhJuHr0HrINtVPftKS8JVbFZLwVZTEnNvPJfun1t69RVG3ejTueBJ4U+l3RrBT1/oR7OJMvHwxEr0Hl8APbocBVQTw0BBK2Xye9pXTa5bNi4jcdFPf5h6VyDttCobLVh/BLlKQhK8/gp3L4eVb2RPeEpLwaTXnZdyydUY5ScLXXUFzb6+s1p+nx7EbS80k53UHeMUSnujU/eptX3WxT96rN5qdyWq7YvYGomcEOyCNAH6yVK6iD65sPk9ryun1y+YjVm7n6lX0kWaT8NUetR33fo+bhCR84pRQHMEugSS8UVlnkaskvLbmXl351/td2tEBPkLYJtXrJjw8MwGYQQA/KepU0QdaNp+nBeX0VcvmRURefXXe4J5UqKIPtyd8tfR7nch/0pLwdc4NkvAaVGPO7eeS5VOr1+tdvKg0ebtvr6hqRr+Vm4jonD/zwBOlS4bbAT5hojrAJ2h+iVYHrjN8AWaUC8WGb9DnV/sA2EAAP3G0kvDeTvNeX6Dl9PrTvMdZvKv5n4SvMwR9tSdsl/X2CfEIJ/rZXmAfrbnf1+iakSi2NHgykITXUHRK5LcVloPnqandBVv37f2UwfQ7HeDjimeDn6gO8BHN785e9O7XBVgooF0FtBHATxDdJHzLEu9pwZXT10m82+sLZzUJr9sBfiydhK/ZAb7BaFw04wTfghxF9RsZkvBGpnYprAAAH/NJREFUZZ1FiVMrliUz10+13rTzzpPzborPE93gczqTe90BPpLe86i9imYNSKB+PkEtCd+qSePoAA9kIoBHtrYm3hMS5fQ+h/E1Eu9jft0CXSbhoyz01NTuCk/bDfZ+lxrhQUGZcbU55OL7U2kmuaqzfGsiCV9b2ZfrIurT34Tz91Ze9n6Xa+28chGytx3gEac2mp3D6N33meSANiOAn0TFVfStGa9Oneep+Krj1cW5uMVqVdGH1RO+ZvrdyCuAtibh01dctRHsIiThyymdSPnnj51Ta/v7pvJNFLyfcjmHXP3Q18H7weA6wOfVz09yB/hIWSG9y9y7t3G7tzsGmEQAP1lKq+hbXzafx9uR7eqVzUesTyRTbUL4MUPzXfnISOF9W/Jj3YF0RYa9rAesVn77DSq/prw8qaL4JPMMGQvo5ZS6Og1Faia5bAF1gBeRZ257Jq9qegI7wCfkvJFxFr3nrt/eTHL6WthKAHEE8BMqMwk/IWXzBXwrpzdRNh9xdDPzdii7yiPYNVs/n9C++eQsnQBU0degds74WUXvkK/182i5shcxBK7ARCCAnziZSfgJLJvP40k5vYmy+Yijp0wbQ9lVHsFubFxFX2cEu2aHr4u0ez45s20OVfS1lZ05Lk6tAM5ex6UxiXHs6mvqHUTmOBpfmjokInuHzzeyS8FJJeGt19k5tTV8AyPYAXkI4DG5ZfN5Gi+nN1E2Hwngpu5yKDvdB26v0u/V+J+Et4QkfCU6Z4uHSXhXEbXH6XftT6AtPXQmSGo0O2/PRgC2EMBPrucef7zF07zX10g5fb1p3jO5fjhr2VB2ptLvpt4F1B/Krs4Q9BGrA9HXGcFujCR8rvJvTe3EMBr15bxmKtlEsy+njMS9Fl8RbuUwgxvBLpJOujKCXVyskL7BSeN6/g1E78+eAHa9rekdQK7+Tf3r5Xorq35I7nv85rW1tZ0nNuuKdzz99CtWthS89U99Kvph1e12TYTuxu5kxWNKXfOtQ3/ywBMf3hxRySLd9FfmDMOlTD1bHzhwwMiq7hoMPM77VWcvctZNwl//h5+V4Qcs7Uwt3QpTo0fvSr6a/ftB18Mn3cz3TTfd1JfUfXAwkOEwOsD0UVifrdD4Zeigk05YI9gVMDWCXeZ5FZa9e1++d3Wzibt4cbeIlRccOWfOoLgBabqaiaIStB8ZeH/t/rN/srfyt9567R3veOut33xtx9NP73j6aXsbCp37z8dQ4t1Y8bxq9L7l/HvWtJaP4rd0FJfXAb7C0/PU1KHx/9fiQ+/3mqKgKKD+8Dl6ugFnhST869977Mdr3nXB/fHa8z8+80SFP7zttme0lv/cA0/otRjRSTUYyFYrYaGrapUVvnu49kuxO4ccZedWPaN56urz7u2Vru/2ho98affez6/t/fzaxYu77W3IVQXH8Lbbkm8b6QAPFCAD76/f/fm7dtx7Yq5eKWme4e37vrL35amp3fJZEZGBtOT1PETEVPQeD91Vb6JPDs488M4zD7xTa+S5CjlY/QfoNvR+j5Lw3+31tA7fQfQ+GAzuu+++Xq+nvq1Kifee1vdYIYaPnlb9SVdG+/Plr7x8/nuP6RxOQQuQ/U8P33ujyM6H773x77U+NPun1quvzl9/fSf9+6ee6olIt5vcgS/KPul+oELFgqJmq2Cicp5vf/vbBw4ciFqDqfNrqx/eWaG1r9+Vpo68puzjz3zr4w9deEhK5rutp8GacwO2zsCuiNy1sysidz1l8XvcN9y3b7hPvUms2rAD0EMG3l8f/9v/srTmaEyvz8Xe2vbsFF+hOU1E7yJSb9D4Ui6fno2n300V0tdfiQ3z8/PW1u3uS/cndJdY7qvSiABZ50nh5aMXuptTpyt75t92pSsiu4a2aulNXYMO3hIWp0+9bUzSzHWA3zxhXn3VXntlUfwOeNdgMP4G7d0Zy5pEs9utuTZeBGCCEMD7blF/LGVF3e6FgVwY5957so8wvhXq3sN60htH7wMZVCtgiwrgLbH93Gn8wdr4uwBvO8OXBWNRZjQ3siocwS760l0cuA/jfo33YfwArdylv+wjSl0+D8fW/LC1O06xce1G1OCU9tzJrPX4Ym+fiNw1GEQ95I3Xnti47hrpAO9bAxJ9U6WDldTuAB9w7j0qWIh+jofuDmJ4yW4STX2GqZqgrfEXq64wvC8XqIAA3muW6ucT4vXzxPCBq1s8XznxHmc1Ce+Gz73fvc2b2a/Vd3HgXiXhxzujmYTX/qCi9HtTSXiD/mJg9+vz9urT1ZoDURZq9B4P3SXri7P9VdpvEgP7RgAfEMAHwGwSfpiVXEqk4g1uDs4ZiN4rJ97j6iThM0ewc5M18rP3e1q1T8NUx9eCmeRyk/DRcHo9Eb2REdNLtj8Jn9cVXyEJr/3hpFPuTSXha4rS73FmWwxPstbRu8VxM/XG3Q9K+sQozGF6ciBVZqOsLsjoPR265zXdDj7M3Cax1kxyuc07I9gBxQjgfWcpCZ85OTbl9IGr0ZV0e9l8/V2xl4R3kDjyOf0e8TZ7Zi0JP0j9YFGzSfi8B2XlJHzF9Hv65+CM0++vVJl4r5y3152u1hyIppCOWjF0L/grg2w2iU7bdqA1COAnS2b6PY5y+mBVL543UjZvmydZo5pCSe97ow1fupZ013djvLyC6oxgp7IGb9uNxkewC0jtEeyMTarqRl5392JuOsNnMb65asNPenqlA5YQwIfBbBV9Zvp9jHL6YNWK3o2UzScYH8ouuOHrxmxk9b0NTqpFZfkj2KW/9HZW0atE74VV9Aofy/YrKK9a3nEVfbXCjfhfpevnzSbhLV1r7kew87bRKFV1BLuQjre0u3sx2zF8qkmseTtO72T9+3swr2mAmgjgA+BmKLsEyumDUuVubbxsPsFgFX3Qs8dZ4m0RLEPZVaMSvStU0deqn8/7TRAYvk5Raw5EQTBd39W7uxezF8PbaRLzjtHWHJBAaxDAB6N+Er60fj6BcvpAVKkPdFY2XyEJnzmCnYScfrfH23xa/dLostdSrUrCa1XO5yThDQxfp/6v2zfc8HBT6fR7XP1rxLerbDyOXdQkTp1fU/xD3w7E8tBrYUTvpkL3+BrGa661Z1nMNYk5+xaNcjoQ8bhDH+ADAvgwGEzCF9fPJzBRfCA07nP9m/pWy+bjgptPzkH63eBrAt3nPFND0Me3nvmMmJuEHwxEuvf3XtZJsGSuqlVJeK3ovTAJn/+x5DzK5yXb3STh897yKE4FP7a6+nJm+t3gKz+zbw/tvivs9URk3/3Zl1hw6feqHeCDid7HP9cP3eOrSq+/vtwGKtqa9rYMfjV+vZkCHCCAD0mdJLxu+j2OVLzH9O5bjYxXV7MnfGtmj7P0dsC3rNpYvSS8yt+2IQlfbdS6VBJe7aOIhQcqCXY3PeHr9LkYDAbrqydKF6tzjdi7vhx3gPe2oVCh2QE+gOi92kh16uzl4bc3idV2W2WXKlTR+/t1A8YRwAfDSBJeK/0ex8h2XtIrnneWeI8zlYRn9rhM3ubTdKOynBHsClbSqiS81oZykvAGer+r/6tXdkwdyvsnIxeIt1eZrtYcSCHfo/eaI9Wps7Fmc01iwb5ZmQMSaBkC+MCYHY5eF+X0/lG6Q8fHq5t/dd7m/mRTT8InOsC3Jv1ulbe5NRM94Uu2YHn9IjaT8NGaqz0Tx5Lwhnu/V1uyEcW93+vz9soad4NXXN7bAxn3xIle+a2uvlxvfV5H78a7u5eyNMpAvSaxZGeGMhSRgXQpjAcKEMCHpHISvk79fALl9N5Qvbc1Ps17/SR8y9LvZt8XeJtVq1EarXhuh52Er/MQrFOqkN03VSXBbjUJb+rlTpR+L11b5RjG+PXl4HXhiaxuBd42FAU0O8D7G727D93TO2BqVTlNolbFe+mxayXhifMxiQjgw1M5CV+5fj6BcnoPqBbPN1I2n6lCT/j2pd/tvSbwNsOWjqy6sluUhihTPF2DTMJX6/qecOLeGzUOfytg0E2qW03C1+kAH6XfS9dQOVKyek257ADvbeOgSK0DvNfR+/hn96G7pc7wsSZR63B090F9ee++d8AqAvjAVEjCG0y/x1FO3xyl6N32NO9a6iThW5Z+t8Hb3Fql8EzrCS/IJLyR6D2WhDfc+73ako3Qmvu9QgDj7ZWlK4gD+VI0lkGVbhGeRu+2R6pTZDyGr9cklkyW8dSgW7IYAAL4yWEq/R5HOX1zyqP32KJB3gjbl35vltk55CJVOlgOBiJyf29bT9esEeyCPGlVGIneI+99j+oE4GOe92lXp9X7vcJpH2rWutcTkS9/bvf4F6EeSBUeNRrORqpTZHVyeBGRKOouWfnknIqAdQTwQWp2KLs4Jop3ruQWmEi8exW9l1bRJ0awE9LvOrx9Umcou/RKTGX1T9xbPolaQoWkekHYH7U2jbQzWun3ChoPuopFDdeV/ftFZOp80asczw+k9kBrerOx2NZ4d/c8xmP4Sk1iyUexNYJdfDFP72tAswjgw6NVRW+pfj6BVLwrJU8qPifeTc0n1w7GM/+ePCOmZVXRRxWSmYMeVXhWC6aK3nj0fveTD5YvVPth3UYVfek7naj5UhgoIWOd0Q+Z3Tcaf8nV1Ah2IVIbwc6v6M7P0H3MYAyf1YhVmLm9mMqn59cJADhDAB8qrSS8jfr5BEa2c6U8evct8R6nOJRdi+vnrSb8G49P8mgm4W33nK+oThLeePQ+PuQTKveCwUDq1c/bqL2vPIJdon5eZT1aoZTt68hsI/DG3Q9K6jSITjNvGwR1hSPYedT13ZPu7qXM5uG3mjXbkbapEU+B9iCAD5JiEt5N+j2Ocnqbcu9hXo1XV0A3CU/9vBZvHxmV47TKT3i+J+EtRO+RQWo+uRLV0ukeDmVXuX5eMW7x9mrS1ZoDSfElevetu3spUzG8fmtW4ZPx/cMEmkIAHzDFJLyD9Hsc5fR25BbP+1w2n6k0Cd/i9LsD3ubc8pLwqRHsbM8eX0udJLzR6H3bwaok4eun0D0ZAE9r+Lo4xcjK2ytIV2sOJIsX0bu33d1LGdxPtSax/qnY4pMZqIIAPlQV5pNzhnJ6O4qid5/L5uMKkvCJEexan3638QYh70OzMQR9fIulocK2JHzWQPS1+ZuEjx5wjc9FNz7koiT89u+lTiLdYBK+/qCGeel3U8MlWmp8jF/yUQv24gsvbP53r3di9UR8CPqw4sldw2udqAs7wDcfvYcbuo/VHjtQJNGsDboiw/xBN8o+n15vKMOnBt3Uk0zBHxLVY3IRwIetIAnvvn4+gXJ6czLuUqGUzWcqSMJPQvrd9osDbzNvhfGVkX32LglvZOz6lIzDLErCDwamkucGk/DVOsDnpd+11lZwgTi4dhy8N7zQveBtI6AlqwO8F9H7+OcQQ/eE+qfKvuE+J2F21VcDQBsRwAdMJQnvuH4+gXJ6EzKK54Mrm49T6Qnf+vS7Pd4+TaqFWHV23rskvLWu75I4WJWe8PVT6J70hK8ze5zK1eHtFaQruAPpdrsisrpaUJvTcPQeykh1iox0hldr3Gp+UGF/zoANBPDBy0zCN55+H2OieBOu3b18nuZdi+Jw9Da0tfd7nLf5tygJ35XdEnsP9d73FM1irbsFc6vKpZJXtxa95x5gXhLebN/1BnvCV+79npZ5gXh71ehqzYFs12T0HtxIdYpMDWiX3yS28lQEmkcAH7biJHyz6fc4UvFV9bb/R8CJ97h0Ej6K56/b+aCbHfAk/W7pVYK3D5fpJHwUc27NZ15/t31JwtvMvUvmYWYk4WNP5KaS54n1RC2SVltUs5t6afq9dP3FV4e9a8fSxX7gwIH3P/LI+vr61Pm1+Azw3jYCeaIdfu38mmR3gG8sem9Bd/di9WP47a1cejZ4pY9rKEMpb0x6OT8DE4cAvg205oRvCiPb6dtWPN+OxHtcXhLe6uORJ+l3B28QvM3C9Xo9ka6IZD3qmdmCndVuU5CEtxm9lxxaOgn/cCyoM6V+El6lP0XUyvVv6kf/WZp+rzyrfMTN9eLgwv+bp7q2N+FGrAN8M9F760P3MSN5+H3DdHtu8JrK++Tb+Y0ApdoRwK+cXDhy5J5NRxZOLq80vUcOpZPw/tTPp1FOr+xa9B70eHV50kn49y6/5WbTnqTfrfL2QTMdZRmtn5fGk/CWc+9ScIB5PeHN9l1vsCd8nd7vaelYxdurRldrDmQ719H7+OcWh+5jNWP4/ObO+Ofm6YtpwLEWBPDLC/ccXlwajbb+e7S0OH94YbnJXfKCP/XzCZTTK9uM3mP/3fJnCGnto2eT7M0hF4nW/J/9vsbfZMwk14bv3X70DgNa3MjE55Bri4xhXK1q2Uh16gzk4QddkeTslUp6PRF5alBcPDIpXwSgIvgAfnlhfklEOnP946dOnTp16nh/riMiS/NHTk5SGv5aFb3P6fcxyunL9Lb+p21l8wnjKno36XdP6ued8buKXsR8+v3aFuysdpu8Knpr0bvSQcWr6H9w5gk7e+J6KDuDw9fFjS8Qb68UXR/9SsEQ7iFy+r20daQ6dTUP+d5tHXbsfXctuVqBOgIP4FdOfnNJRGb7xw7OTIuIyPTMwWP9WREZDc9NTASfrqL3Nv0eRzl9jl70f+0rm08YV9FHYfxrsw9N4Oxx9t4pePv0Oa6iX1tbk80R7MzuajNV9FE8bzn3XnJo16rot+IQGxXvlddZZwQ7s/Xz6asj3KE3ojZtfX09+k9vL/xS8XHsRMRl1/fJ6e5eKjrwCq+0Yk3fuDO86meoNoJdfIXE8Jh0YQfwK+eGIxGZvXNm269nDs11JiuCF5FAhrJLoJw+U/zW1NbofczZfHK+pd/dvErwNrUYRe82OU3Cq0wsV4/G4URJ+B+ceeLJD++0tj/Vk/C6o82pp9+rjWPn7BqxfclPnV/706lDVjfhTPfCuJraRfQ+/nmSQ/eECtfFxYu7z6w9LyKuAmy+KUyusAP4ly5nxe8i03tuFZHR5Zfc71JToiR8EPXzCUwUH9eTfb2tt9dtLZuPi5LwDF9nibdPojUHDFfZguX1i8QyTq66visdVHwoO3sDzjkeys5s+j3O9ggRjrXjQPbuHXcEsHs4E9vdvVjlzvDbG0A+TMCitzW9A3WsXL4kIp09N6f+5eY9HZHRpcsrsllZH6Jz5849++yz6stfffPq//7liyKysfEHz8knre2XFR+RW/7vt/41+vnP5deb3ZkG3Shy5cqNIt+5/ae3P27nS3zz6tV3vP3tNtZcze3Pv7UhIiJX9u/3LUnuhtWj/skLL7jZyptXrz7+SY0z9vbnN9/aPP7JF8TCqf4b7/uRyB7jq024W1aPPvb2u0VEpCtT//yYxS3+7Keqn9JvX/jF9+UfxPKXfvXNq/JH/ygiR5/9juKfRPUA6ufJzvf96F3r67/YsUP9QF5QP+H37//J176munAI1tfXQz+QvevrIvLzH/7i7Ke/87Of3m6jZYgk7oPvf+SR0D86w7auDq1WXURuf/6tc/Kc7H3uypV/0N3m0T1K7eeHPrT59vBZ5ZZHRBYuX9bdH8BnQQfwFZ0+fTrvn0bXBrP3wuuvv66+8Lsf+Prakw+LyJtXr1rbI4tueWFGRF685d+b3pEm/fKNG2575YMi8qZY/BK9OkN+eMsnfu/F7//wlk/coHO2V3DHHXcEfYFXs3Nu7uff+MYNn/mM1W1FZ5TWefVrO3b8an195fen3nzFytm4uvrBXbv+x8aa4z4qu/5txysi8tH1Xeuybm9Dr7zyO6LcJlzY+7F9F39w896P/YfNL/2uB75+5smHb/jvG3QPXP08ifp1f+SP/0r97L169aqIfEbthI/2xPbVMWa7/fn03+3669dcHIgDq6sfVD/hqxl/++KkKQ5OdO/Q/av3b7zrJ9e9ceWKdnnvv/xql6i1JBsSvfO/Nu6DCmd3f98eM9BW121sbDS9D5WtnDxyeHHUmTt+7OB01r/IbP/U0WR5fb7Tp0/v37/f8D7WMBqNOp1O03uBtuG8gg2cV7CB8wo2cF7BBvXzKkolehV0ICxh94EvkVVcDwAAAABAiFodwAMAAAAA0BZBB/D5g81Hw9PfuifcEewAAAAAAIgLOoCPBpuXS5eT873nD08PAAAAAECQwg7gp+/odkRGiyeWt/16+cTiSKTTvYMEPAAAAACgJcIO4DcjeFmaP7KwHKXhV5ZPHplfEuJ3AAAAAECrhD4P/PTBL/QvH55fGi3NH16K/X62n5paDgAAAACAcAWegReR6Zmjx/tzs9fmXezMzh3Xmf4dAAAAAAD/hZ6BFxGR6ZmDR2cOHm16NwAAAAAAsCb8DDwAAAAAABOAAB4AAAAAgAAQwAMAAAAAEAACeAAAAAAAAkAADwAAAABAAAjgAQAAAAAIAAE8AAAAAAABIIAHAAAAACAABPAAAAAAAASAAB4AAAAAgAAQwAMAAAAAEAACeAAAAAAAAkAADwAAAABAAAjgAQAAAAAIAAE8AAAAAAABIIAHAAAAACAABPAAAAAAAASAAB4AAAAAgAAQwAMAAAAAEAACeAAAAAAAAkAADwAAAABAAAjgAQAAAAAIAAE8AAAAAAABIIAHAAAAACAABPAAAAAAAASAAB4AAAAAgAAQwAMAAAAAEIDrNjY2mt4HX5w+fbrpXQAAAADQcvv37296FxAqMvAAAAAAAASADLy/Tp8+zcs5AEGgvQIQCtorAEEjAw8AAAAAQAAI4AEAAAAACAABPAAAAAAAASCABwAAAAAgAATwAAAAAAAEgAAeAAAAAIAAMI0cAAAAAAABIAMPAAAAAEAACOABAAAAAAgAATwAAAAAAAEggAcAAAAAIAAE8AAAAAAABIAAHgAAAACAABDAAwAAAAAQgLc1vQPttLJ88sQ3h0ujkYiIdDqz3fsOHZyZzljw5MKj15abnctZbGx54Z75pdn+qaMzmVtdePSbWytTWRsAGG6vrLV+ANBMe6W8UQBw4bqNjY2m96FtlheOzC+NUr/uzB0/dnB6+4L3zC8lF8uLzmN/kLnIyskjhxdTW01vFACuMdte2Wv9AKCR9kp5owDgCCX0pi0vzC+NRDqz/eOnIsf7cx0RGS0eXlhOLCginbnNBTcXW5o/cnIlY70rmbej8b+efHRxlLG20eKjmWsDANPtla3WDwAaaq+UNwoAzhDAm7Vy8ptLIjLbP3Z0XFw1PXPwWH9WRGTp7HJ6wa0qrPFio+G57c+wK8snF44czo/eRVbODUeqawMAETHdXllp/QBARBpqr5Q3CgAOEcCb9dLlkYjM3pksA525c1ZE5NLlrVvHZsSdWHDm0Fwn8Qy7cvLI4fnFpZF0ZvvHo1uG6mZv3tMRkdHll6ofD4D2MttemW/9AGBLI+2V6kYBwCUGsTNr5uipU0cVlsu7J0zvuVVkNLr8kkisZ9V49JXls9mru3lPR2S0dHb56ExsjdFGOntu1joEABPCbHtlp/UDAJGG2ivVjQKAS2Tg3Vg+uyQine4d0YPpyuVLkh1aR1nz2Evd6YPHTh07WjLa6fQd3a3+XcvRX64snzwyvyQis/cxyAoAHdXbK/trA4A4q+1VzcUAwAoy8C5sdaKyF0tPHzx2fM/Co/NLi/OHF8e/7cz2v8CgzgB0mG2v7Ld+ACZXI+0VzRqAZpGBt2954fDiSKQzd0g5lq7Qb33l3NlLyWlORpfOvkQyC4A6s+2Vm9YPwGRqpL2qsFEAMIoMvGXLC0fml0Q6s32d+UJ1+61vTQIfpdynRURWlhcenV9amj98ialKASgx2165af0ATKZG2qtqGwUAowjgLRqH1bZD6OUT6c1Mzxw91pd75pdGiyeWD1JID6CQ2fbKWesHYAI10l7RrAHwBCX0tiwvFDT003tulexCrmj41Fv36NwaNodsSQ+mEs2awqBQAIqZba8ctn4AJo7D9kp7MQBwgADehpXlhXvmo6nbT2U39HmDoeYPnwoANphtr2j9ANjjur3SWQwAHCGAN2954fD8UvSWNrdyPZr3bbR4Ynn7n0a18JoTk2y+bx6eS96uotWR0QKQx2x75bz1AzBB3LdX6osBgDME8IatRNOvl9ZYXZu5fSE5c7v+E+zMnbMiMlo8PF6ZyMrmSCsMlAogh9n2qpHWD8CEaKS9Ut0oADh03cbGRtP70CbLC/fMLxX8+2z/1PgF7srmW938JXJWn7XI1tgqSdx0AOQw215Zbv0ATLRG2iudjQKAK2Tgj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class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a14-media-attention-to-the-wealth-tax-in-german-media-outlets" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a14-media-attention-to-the-wealth-tax-in-german-media-outlets">Appendix A14: media attention to the “wealth tax” in German media outlets</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>mw <span class="sc">|&gt;</span> <span class="fu">mutate</span>(<span class="at">yearmonth =</span> <span class="fu">as.Date</span>(yearmonth)) <span class="sc">|&gt;</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>               <span class="fu">select</span>(yearmonth,no_articles,outlet) <span class="sc">|&gt;</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>   <span class="fu">ggplot</span>(<span class="fu">aes</span>(yearmonth,no_articles ,<span class="at">color=</span>outlet)) <span class="sc">+</span> <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;Number of articles&quot;</span>,<span class="at">color=</span><span class="st">&quot;&quot;</span>,</span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>                       <span class="at">title =</span> <span class="st">&quot;Level of attention in the FAZ, Handelsblatt and Die Welt&quot;</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>mw2 <span class="ot">=</span> mw <span class="sc">%&gt;%</span> <span class="fu">filter</span>(outlet <span class="sc">==</span> <span class="st">&quot;hb&quot;</span>) <span class="sc">|&gt;</span> dplyr<span class="sc">::</span><span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">10</span>)) <span class="sc">|&gt;</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">names_to =</span> <span class="st">&quot;series&quot;</span>,<span class="at">values_to =</span> <span class="st">&quot;value&quot;</span>,<span class="sc">-</span><span class="fu">c</span>(yearmonth)) <span class="sc">|&gt;</span></span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">yearmonth =</span> <span class="fu">as.Date</span>(yearmonth))</span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>series[mw2<span class="sc">$</span>series<span class="sc">==</span><span class="st">&quot;no_articles&quot;</span>]<span class="ot">=</span><span class="st">&quot;total&quot;</span></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>cols <span class="ot">=</span> <span class="fu">c</span>(<span class="at">total=</span><span class="st">&quot;grey&quot;</span>,<span class="at">spd=</span><span class="st">&quot;red&quot;</span>,<span class="st">&quot;cdu&quot;</span><span class="ot">=</span><span class="st">&quot;black&quot;</span>,<span class="st">&quot;fdp&quot;</span><span class="ot">=</span><span class="st">&quot;yellow&quot;</span>,<span class="st">&quot;grüne&quot;</span><span class="ot">=</span><span class="st">&quot;green&quot;</span>,<span class="st">&quot;linke&quot;</span><span class="ot">=</span><span class="st">&quot;darkred&quot;</span>)</span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>mw2 <span class="sc">|&gt;</span> <span class="fu">filter</span>(yearmonth <span class="sc">&gt;</span> <span class="fu">as.Date</span>(<span class="st">&quot;2017-12-01&quot;</span>)) <span class="sc">|&gt;</span></span>
<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(yearmonth,value,<span class="at">color=</span>series)) <span class="sc">+</span> </span>
<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="at">alpha=</span>.<span class="dv">6</span>) <span class="sc">+</span> </span>
<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> </span>
<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_linewidth_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)) <span class="sc">+</span></span>
<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> cols) <span class="sc">+</span></span>
<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="st">&quot;serie&quot;</span>), <span class="at">linewidth =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb29-17"><a href="#cb29-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;Number of articles&quot;</span>,<span class="at">title =</span> <span class="st">&quot;Handelsblatt (German economic daily)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>mw2 <span class="ot">=</span> mw <span class="sc">%&gt;%</span> <span class="fu">filter</span>(outlet <span class="sc">==</span> <span class="st">&quot;faz&quot;</span>) <span class="sc">|&gt;</span> dplyr<span class="sc">::</span><span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">10</span>)) <span class="sc">|&gt;</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="at">names_to =</span> <span class="st">&quot;series&quot;</span>,<span class="at">values_to =</span> <span class="st">&quot;value&quot;</span>,<span class="sc">-</span><span class="fu">c</span>(yearmonth)) <span class="sc">|&gt;</span></span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">yearmonth =</span> <span class="fu">as.Date</span>(yearmonth))</span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>cols <span class="ot">=</span> <span class="fu">c</span>(<span class="at">total=</span><span class="st">&quot;grey&quot;</span>,<span class="at">spd=</span><span class="st">&quot;red&quot;</span>,<span class="st">&quot;cdu&quot;</span><span class="ot">=</span><span class="st">&quot;black&quot;</span>,<span class="st">&quot;fdp&quot;</span><span class="ot">=</span><span class="st">&quot;yellow&quot;</span>,<span class="st">&quot;grüne&quot;</span><span class="ot">=</span><span class="st">&quot;green&quot;</span>,<span class="st">&quot;linke&quot;</span><span class="ot">=</span><span class="st">&quot;darkred&quot;</span>)</span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>mw2<span class="sc">$</span>series[mw2<span class="sc">$</span>series<span class="sc">==</span><span class="st">&quot;no_articles&quot;</span>]<span class="ot">=</span><span class="st">&quot;total&quot;</span></span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>mw2 <span class="sc">|&gt;</span> <span class="fu">filter</span>(yearmonth <span class="sc">&gt;</span> <span class="fu">as.Date</span>(<span class="st">&quot;2017-12-01&quot;</span>)) <span class="sc">|&gt;</span></span>
<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(yearmonth,value,<span class="at">color=</span>series)) <span class="sc">+</span> </span>
<span id="cb30-11"><a href="#cb30-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="at">alpha=</span>.<span class="dv">6</span>) <span class="sc">+</span> </span>
<span id="cb30-12"><a href="#cb30-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> </span>
<span id="cb30-13"><a href="#cb30-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_linewidth_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)) <span class="sc">+</span></span>
<span id="cb30-14"><a href="#cb30-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> cols) <span class="sc">+</span></span>
<span id="cb30-15"><a href="#cb30-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="st">&quot;serie&quot;</span>), <span class="at">linewidth =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb30-16"><a href="#cb30-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb30-17"><a href="#cb30-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>,<span class="at">y=</span><span class="st">&quot;Number of articles&quot;</span>,<span class="at">title =</span> <span class="st">&quot;Frankfurter Allgemeine Zeitung (Germany daily)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img role="img" 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gAoQaAAAEoQKACAEgQKAKBkVIF+BgCgD2MKaAeMK9DlfxfHLHAIVoKQw8NKECs5zATZRQ4E6oJABcjhYSWIlRxmgiBQBQg0NeTwsBLESg4zQRCoAgSaGnJ4WAliJYeZIAhUAQJNDTk8rASxksNMEASqAIGmhhweVoJYyWEmCAJVgEBTQw4PK0Gs5DATBIEqQKCpIYeHlSBWcpgJgkAVINDUkMPDShArOcwEQaAKEGhqyOFhJYiVHGaCIFAFCDQ15PCwEsRKDjNBEKgCBJoacnhYCWIlh5kgCFQBAk0NOTysBLGSw0wQBKoAgaaGHB5WgljJYSYIAlWAQFNDDg8rQazkMBMEgSpAoKkhh4eVIFZymAmCQBUg0NSQw8NKECs5zARBoAoQaGrI4WEliJUcZoIgUAUINDXk8LASxEoOM0EQqAIEmhpyeFgJYiWHmSAIVAECTQ05PKwEsZLDTBAEqgCBpoYcHlaCWMlhJggCVYBAU0MODytBrOQwEwSBKkCgqSGHh5UgVnKYCYJAFSDQ1JDDw0oQKznMBEGgChBoasjhYSWIlRxmgiBQBQg0NeTwsBLESg4zQRCoAgSaGnJ4WAliJYeZIAhUAQJNDTk8rASxksNMEASqQCXQH//K4eHhB77TWI5ABcjhYSWIlRxmgiBQBRqBvnzPYc5tn/JXIFABcnhYCWIlh5kgCFSBQqCvPXT49u9kP17+/0feGgQqQA4PK0Gs5DATBIEqUAj05Xve+t3lj1fvazRBEagAOTysBLGSw0wQBKpAIdAXD9+R/1g2RD/irUGgAuTwsBLESg4zQRCoAlqgqSGHh5UgVnKYCYJAFQzpA32HvwaBCpDDw0oQKznMBEGgCjSj8K99uxiF/8X1GNJnKi4ClDz11K4TgH1mKdCXf7kQ6NsaE0FpgQrMNcfTTz8tr5hrhQSxEoQWqAJVH2je+Fw2Q+kDjWGuORBoLFaCIFAFCoE+efjB8qffCYpABeaaA4HGYiUIAlXQX6CvPVS2PF++x59Jj0AF5poDgcZiJQgCVYBAUzPXHAg0FitBEKgCLuFTM9ccCDQWK0EQqALVnUgMIvVgrjkQaCxWgiBQBZppTM8frvigvwKBCsw1BwKNxUoQBKpA9TzQf5s/D/QXeB5oFHPNgUBjsRIEgSrgifSpmWsOBBqLlSAIVAECTc1ccyDQWKwEQaAKEGhq5poDgcZiJQgCVYBAUzPXHAg0FitBEKgCBJqaueZAoLFYCYJAFSDQ1Mw1BwKNxUoQBKoAgaZmrjkQaCxWgiBQBQg0NXPNgUBjsRIEgSpAoKmZaw4EGouVIAhUAQJNzVxzINBYrARBoAoQaGrmmgOBxmIlCAJVgEBTM9ccCDQWK0EQqAIEmpq55kCgsVgJgkAVINDUzDUHAo3FShAEqgCBpmauORBoLFaCIFAFCDQ1c82BQGOxEgSBKkCgqZlpjgsINBYrQRCoAgSampnmQKDRWAmCQBUg0NTMNAcCjcZKEASqAIGmZqY5EGg0VoIgUAUINDUzzYFAo7ESBIEqQKCpmWmOC09fChh0phUSxkoQBKoAgaZmpjkQaDRWgiBQBQg0NTPNgUCjsRIEgSpAoKmZaY6lPRFoHFaCIFAFCDQ1M82BQKOxEgSBKkCgqZlpDgQajZUgCFQBAk3NTHMg0GisBEGgChBoamaaA4FGYyUIAlWAQFMz0xwINBorQRCoAgSampnmQKDRWAmCQBUg0NTMNAcCjcZKEASqAIGmZqY5EGg0VoIgUAUINDUzzYFAo7ESBIEqQKCpmWkOBBqNlSAIVAECTc1McyDQaKwEQaAKEGhqZpoDgUZjJQgCVYBAUzPTHAg0GitBEKgCBJqameZAoNFYCYJAFSDQ1Mw0BwKNxkoQBKoAgaZmpjkQaDRWgiBQBQg0NfPMceHpSwg0EitBEKgCBJqaeeZAoPFYCYJAFSDQ1MwzBwKNx0oQBKoAgaZmnjkQaDxWgiBQBQg0NfPMgUDjsRIEgSpAoKmZZw4EGo+VIAhUAQJNzTxzINB4rARBoAoQaGrmmQOBxmMlCAJVgEBTM88cCDQeK0EQqAIEmpp55kCg8VgJgkAVINDUzDMHAo3HShAEqgCBpmaeOXKBBgw6zwppwUoQBKoAgaZmnjkQaDxWgiBQBQg0NfPMgUDjsRIEgSpAoKmZZw4EGo+VIAhUAQJNzTxzINB4rARBoAoQaGrmmQOBxmMlCAJVgEBTM88chTsRaBRWgiBQBQg0NfPMgUDjsRIEgSpAoKmZZw4EGo+VIAhUAQJNzTxzINB4rARBoAoQaGrmmQOBxmMlCAJVgEBTM88cCDQeK0EQqAIEmpp55kCg8VgJgkAVINDUzDMHAo3HShAEqgCBpmaWOYp59Ag0DitBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKkCgqZllDgTaAytBEKgCBJqaWeZAoD2wEgSBKhhXoBcBcs4/9cLy/089tesckIgnnhinHATqQgtUYJY5Vi1QuQk6ywppw0qQPjm+8Y1vjLJPBOqCQAVmmQOB9sBKEASqAIGmZpY5EGgPrARBoAoQaGpmmQOB9sBKEASqAIGmZpY5EGgPrARBoAoQaGpmmQOB9sBKEASqAIGmZpY5EGgPrARBoAoQaGpmmQOB9sBKkB45LiDQEgSamjnmKP2JQKOwEgSBKkCgqZljDgTaBytBEKgCBJqaOeZAoH2wEgSBKkCgqZljDgTaBytBEKgCBJqaOeZAoH2wEgSBKkCgqZljDgTaBytBEKgCBJqaOeZAoH2wEgSBKkCgqZljDgTaBytB4nNcuPDHCHQFAk3NHHMg0D5YCdJDoN9AoCUINDVzzIFA+2AlCAJVgEBTM8ccCLQPVoIgUAUINDVzzIFA+2AlCAJVgEBTM8ccCLQPVoIgUAUINDVzzIFA+2AlCAJVgEBTM8ccCLQPVoIgUAUINDVzzIFA+2AlSHSOCxf+GIGWINDUzDEHAu2DlSAIVAECTc0ccyDQPlgJgkAVINDUzDEHAu2DlSAIVAECTc0ccyDQPlgJgkAVINDUzDEHAu2DlSC9BJqNY1AE6oJABeaYA4H2wUoQBKoAgaZmjjkQaB+sBEGgChBoauaYoxKoaNA5VkgrVoLE5rhwIUOgFQg0NXPMgUD7YCVIvECX/kSgKxBoamaY48KFSwg0HitBEKgCBJqaGeZAoL2wEgSBKkCgqZlhDgTaCytBEKgCBJqaGeZAoL2wEgSBKkCgqZlhDgTaCytBEKgCBJqaGeZAoL2wEgSBKkCgqZlhDgTaCytBInOspoEi0BUINDUzzIFAe2ElCAJVgEBTM8McCLQXVoIgUAUINDUzzLG+EQmBxmAlCAJVgEBTM8McCLQXVoIgUAUINDUzzIFAe2ElCAJVgEBTM8McCLQXVoIgUAUINDUzzIFAe2ElCAJVgEBTM8McCLQXVoLE5Vj6E4FuQKCpmWEOBNoLK0GiBZr7E4GuQKCpmWEOBNoLK0EQqAIEmpoZ5kCgvbASBIEqQKCpmWEOBNoLK0EQqAIEmpoZ5kCgvbASBIEqQKCpmWEOBNoLK0EQqAIEmpoZ5kCgvbASBIEqQKCpmWEOBNoLK0GiclTTQBHoCgSamhnmQKC9sBKkn0DHMSgCdUGgAjPMgUB7YSUIAlWAQFMzvxyb5ykj0BisBEGgChBoauaXA4H2w0oQBKoAgaZmfjkQaD+sBEGgChBoauaXwxGoZND5VUgHVoIgUAUINDXzy4FA+2ElCAJVgEBTM78cCLQfVoLE5Mj9iUAdEGhq5pcDgfbDSpBYga78iUALEGhq5pcDgfbDShAEqgCBpmZ+ORBoP6wEQaAKEGhq5pcDgfbDShAEqgCBpmZ+ORBoP6wEQaAKEGhq5pcDgfbDShAEqgCBpmZ+ORBoP6wEQaAKEGhq5pcDgfbDSpCIHM40UARagEBTM78cCLQfVoIgUAUINDXzy4FA+2ElCAJVgEBTM78cCLQfVoIgUAUINDXzy7F5ID0CjcFKEASqAIGmZn45EGg/rARBoAoQaGrmlwOB9sNKEASqAIGmZn45EGg/rARBoAoQaGrmlwOB9sNKkO4chT8RqAsCTc38ciDQflgJEinQ0p8ItACBpmZ+ORBoP6wEQaAKEGhq5pcDgfbDShAEqgCBpmZ2Odx59Ag0AitBEKgClUBf+/Y9h4d//VcbyxGowOxyINCeWAnSU6CjGHSWAv3xfYcFv+ivQKACs8uBQHtiJQgCVaAQ6GsPHb7tO9lr/9vhbZ/y1iBQgdnlQKA9sRIEgSpQCPTFw7d+N//5/OE7vDUIVGB2ORBoT6wE6cxRmwaKQAv6C3TZAP1IYBUCFZhdDgTaEytBEKiC/gJ99b5VA1QAgQrMLgcC7YmVIAhUQX+BvnzP23/053/z8PBt/6SxCoEKzC4HAu2JlSAIVIFKoJ9cjcJ/sFr0mYqLMC2eemr8Ms+ff+GFpDuAHXH+fP7/xx8vf33iieFFzlCgL+YTmH6UvfZtRuGjMJ3jaaGFOJRaC1RogpqukF1gJQgtUAUqga6ank8yCh+D6RwI1ABWgiBQBZpL+LLlmXeG1lchUAHTORCoAawEQaAKNAItR+HXL9YgUAHTORCoAawE6cqx8icCraGZB1q2QF88pAUagekcCNQAVoLECXTtTwSao7gTqer7fHIzDF+CQAVM50CgBrASBIEqUAj05XsOP/AdRuFjMZ0DgRrAShAEqkDzNKYX7ymmgd7WuKMTgQqYzoFADWAlCAJVoHoe6I8/uVToL3ynsRyBCpjOgUANYCUIAlXAE+lTYzoHAjWAlSAIVAECTY3pHAjUAFaCIFAFCDQ1pnMgUANYCdKRw58GikBzEGhqTOdYCvTC2DtCoD2xEgSBKkCgqTGdA4EawEoQBKoAgabGdA4EagArQRCoAgSaGtM5EKgBrARBoAoQaGpM50CgBrASBIEqQKCpMZ0jgUDr/kSg3VgJgkAVINDUmM6BQA1gJQgCVYBAU2M6BwI1gJUg7TlKfyLQOgg0NaZzPD3+THoE2hcrQaIEuvHnKAZFoC4IVMB0DgRqACtBEKgCBJoa0zkQqAGsBEGgChBoakznQKAGsBIEgSpAoKmxnGNpt0tjjyIh0L5YCYJAFSDQ1FjOgUAtYCUIAlWAQFNjOQcCtYCVIAhUAQJNjeUcCNQCVoK05mhOA0WgOQg0NZZzIFALWAmCQBUg0NRYzoFALWAlCAJVgEBTYzkHArWAlSAIVAECTY3lHFsQaNOglitkJ1gJgkAVINDUWM6BQC1gJQgCVYBAU2M5BwK1gJUgCFQBAk2N5RwI1AJWgiBQBQg0NZZzIFALWAnSlqPyJwL1QKCpsZwjF+ilC+MaFIH2xUqQGIG6/kSgGQJNj+UcCNQCVoIgUAUINDWWcyBQC1gJgkAVINDUWM6BQC1gJQgCVYBAU2M5Ry63sUeREGhfrARBoAoQaGos50CgFrASBIEqQKCpsZwjgUCXnzQE2g8rQRCoAgSaGss5EKgFrARpySFOA0WgGQJNj+UcCNQCVoIgUAUINDWWcyBQC1gJgkAVINDUWM6BQC1gJQgCVYBAU2M5BwK1gJUgCFQBAk2N4RyF20aeSY9Ae2MlSG+BjmBQBOqCQAUM50CgJrASBIEqQKCpMZwjjUDr/kSgnVgJgkAVINDUGM6BQE1gJUg4x+YMQaAeCDQ1hnMgUBNYCRIh0Lo/ESgCTY/hHAjUBFaCIFAFCDQ1hnMgUBNYCYJAFSDQ1BjOgUBNYCUIAlWAQFNjOAcCNYGVIAhUAQJNjeEcCNQEVoIgUAUINDWGcyBQE1gJgkAVINDUGM6BQE1gJUgwR2gaKAJFoOkxnGPltnENikB7YyUIAlWAQFNjOAcCNYGVILMT6OXFjYPLQKCpMZxjKwJtGNRwhewGK0EQqAIEmhrDORCoCawEQaAKEGhqDOdAoCawEgSBKkCgqTGcA4GawEqQfRfos3csFtfduXr9zMHizJ2FQI8Xt+YLTpQyRaCpMZwDgZrASpA9F+jJoqDQ5XHx8giBtmAliOEcCQTaeCA9Au3ESpBQDuf02L1Aj2KQ/vDKweInn8ueWbzuS/m1+5k7s9OlRZML9PTPPv+F+MIQqIDhHAjUBFaCdAvU8+ekBFoa8nhxVyXN0/tTCvTqf/1clr1yx7Khe/3HYgtDoAKGcyBQE1gJMgmBalna8q7q9Su3nymcdpJQoCdFU3fVVZC/igKBChjOgUBNYCXIXgu0kubq9UppCQeRLhfavHKwuOG5q7ev9hABAhUwnAOBmsBKkD0X6KYdeOUguUCPl+Ys+lo/lv8/fx0DAhWwm6M021J44xkUgfbHSpA9F+g2W6Cn9xd7W2nUdXc7CFTAbg4EagMrQfZaoNvtA10585Xbi0IR6CDs5kCgNrASZK8FWhnSkeZqFH61fPVaQZtArxwU0kagg7CbA4HawEqQQI7wNNBJCXQ1D/TZwmnL17dmpw8X80AvLxZ3ZlfvXyS4hD8pukDpAx2G3RwI1AZWgvQX6HCDbv9OpBs3r4s7kZZtz3ym0b8YexDpxnz4PTcno/DDsJsDgdrASpA9F2j9Xvg3VPfCZ6cPLBbXP6J9sEh4GlPOXUXpZ2Jn0iNQAbs5EKgNrATZd4EmITyRftXafeX2xZm7Ats0QKACdnMgUBtYCYJAFYRv5fzoex5c/njl57VnKHkAACAASURBVN70SHRhCFTAbo7KbGPOpEeg/bESBIEq4GlMqbGbA4HawEoQBKqgVaCnP+hXGAIVsJsDgdrAShAEqiAs0Gffld8P/8rPvS9yDlOGQEXs5kCgNrASRM7hnhsItEFIoPks00Kgty+uj30YEwKVsJtjI9DxRpGaD6RHoF1YCdIp0IY/EWhQoMeLxfV/4+B1Xzr9O4voefQIVMJuDgRqAytBEKiC8DzQO8t7OJ85WMTOY0KgAnZzIFAbWAmCQBUE70S6dX0TfPxzShCogN0cCNQGVoIgUAVt98KXAq0eP9oNAhWwmwOB2sBKEASqoP1xdoU5eRrTIOzmQKA2sBIEgSpAoKmxmwOB2sBKEASqIHQJnw8clebs8Ti7izPm/PldJ+jLU0+tfr7wwnjhz59flibvBqaIe2Y8/ri/9oknBha/pwJdDRytBNrjWc2zboFeCLTirFTIVlqgwjz6RhPUboXsCCtBxByt8+hpgYYEeuVgcctzhUCv3sHj7KJAoBkCVWElCAJV0PY4u5sOztz8huXP2OcpI1ALOUIgUA8rOcwEQaAKgvfC/+nBoiTanwjUQo4QCNTDSg4zQRCogvDDRF763E1Le17X43GgCNRCjhCNHGuv5cobyaAIVIGVIAhUAc8DHQsEmiFQFVaCIFAFCHQsEGiGQFVYCYJAFfgCPf3oUZP38LXG3SDQDIGqsBIEgSrwBfrK7Ysm3IkUwXQFOuIoEgJVYCWIlKPdnwgUgY4GAs0QqAorQRCoAvpAxwKBZghUhZUgCFQBAh0LBJohUBVWgiBQBQh0JC4g0AyBqrASRCHQwQbde4F+v09hCHT3OYIgUA8rOcwEQaAKggI9/dwbi0eCLnrcioRAd58jCAL1sJLDTBAEqiAk0MsHi9UzlReLM7HfKYdADeQIgkA9rOQwE2TfBZo/3qN6sMfl6Gd0dhB+nN3qMcp/9sABj7OLYXmmyRKyUiHtAh3JoAhUgZUgQo7aWTFxgV52n4yUWqDHi+urK3ceqBwFAs1BoAqsBNlzgZ4sbtzcUZlYoK/c7rQ6+VbOGBBoDgJVYCXI3gvUeTBncoE6zuRL5WJAoDkIVIGVIHst0OICvrDmMweLM3cWAj1e3HXljsWZ90U+6kOEFuhIINAcBKrASpB5CPS4eHG0evneg0qrWoJ9oDeKr9tBoLvPEQSBeljJYSbIJAR6LQbxL1eX8JeXzc/s9Lhy6Q2P5C3S6HlGTQICXfr6TT8oXr308CK6fAS6+xxBEKiHlRxmgsxCoMfF/1cj48erxySdRH9vu0BoHmje0D1z0003HfRp4SLQ3ecIgkA9rOQwE2QSAtVTCLTqnDxZCbQYVoof5BEICfT0s9Wz7M78fHRhMxZofqYhUASqwUqQZo5Of05QoCtZVoNI+evT+6NnujcJ3wt/+uy7ly3Qm3+9R+sWge48R5htCFT0JwLtwEqQOQi0GhHfgkAVINCd5wiDQD2s5DATZA4C3WILVAEC3XmOMAjUw0oOM0HmIdBmH2j8PE0B4Uvl3vOc/9VyfKlcJ9MTqKu1sQyKQDVYCTIHgXqj8MXw+8mQiaDCdyItdex9MxIT6TtBoDkIVIOVILMQ6JWD5Y/Th6t5oDc+N/I8UASqA4HmIFANVoLMQqDLH86dSEcL9xFNGugDHQcEmiML1DOo1QrZGVaCzEOg2TNvcO6FXzY/r7tzSKkIdByKU010kJUKQaAeVnKYCdLIUT8lpi7QJsdDrt1LZIGeftQZN7ry7n+PQaQuEGgOAtVgJQgCVcDj7MYBgeYgUA1WgiBQBREC5XF2ESDQHASqwUoQBKqgIVBvAL4g9mEl8xXo6lRDoAhUgZUgCFRBswV6uSlQHmfXBQItQKAarASZnUDHoCnQ0//h6OjdB2duXt+H9N4vxhaGQHedowUE6mElh5kgCFRBRB9oPAh01zlaQKAeVnKYCYJAFURMY4oHge46RwtdAh3DoAhUg5Ugfo4Ifw426J4KNDt+0yPyilYQ6K5ztIBAPazkMBMEgSoIXcKrxqcQ6K5ztIBAPazkMBMEgSqgD3QUEGgBAtVgJQgCVRDoA9U9oxmB7jpHCwjUw0oOM0EQqIJAH+hJ/oXJvUGgu87RAgL1sJLDTBAEqiAg0Jc+u1hcdzNPpI8FgRYgUA1WgiBQBcFBJB6o3AcEWoBANVgJgkAVINAxqE41yUFWKqRNoGPNpEegGqwE8XJ45wMCFeGBymOAQFcgUA1WgiBQBQh0DBBoQV4CAu2NlSAIVAECHQMEWoBAVVgJgkAVtAj09Pslz/6H9IG2M0GB1qU2zigSAlVhJQgCVRAS6NUHGESKB4EWIFAVVoIgUAUBgdaH4W9AoO0g0AIEqsJKEASqIHgn0uLMzfljld99sDgT/b3JCBSBItDeWAmCQBWE7oXPvwdp+f+7irs6Y58NikARKALtjZUg9Rxx/kSg4tJXbi8eJnKyuDXr89VLCBSBItDeWAmCQBW0Ps7u8uLG9f9jQKAIFIH2xkoQBKqgQ6D51fsrt/O1xh0g0ILAjUgItB0rQRCogtbngV45yD0a/3TluQp0fa4hUATaHytBEKiC0HciFb2fq67QlUZjQKAIFIH2xkoQBKogINArB4s3PZIPw9+ay5RL+HYQ6IqQQOs7M1ohu8NKEASqIHQn0nFx/9HlxeLMwaJojcaAQBEoAu2NlSAzFGjePXnlIHqepkDwXvg/zS/cT4+LG5GYB9rO5lwTHGSlQroFOtigCFSFlSC1HP65gEBlWh4m8n8uiz195qab3hddPAJFoAi0N1aCzFCgw+FxdiOAQFcgUBVWguy5QE8fXiyuf6S4Oejy4tZnDhbXf2w1Vp6dFDPdjxd3PfOGRT72k3P1gYPF4o3d36yJQEcAga5AoCqsBNlvgZ7en3dHnnn3SqA3H+Rdk55Ab9o8e+7KQfH6TOdNmAh0BKYv0HFGkRCoCitBVAIdaNDeAn1/DNIfnizOPJhdvWOxEuhqYMcT6OLG57Jly/Su4ml0tzyXnX62+0meCHQEEOgKBKrCSpC9FujqyUh5y7IQaHGjkC/QjVRPytvXTzpnICHQEUCgKxCoCitBJiFQLdVY+/FKoM4vG4HeWP1S3om5+aswCHQEEOgKBKrCSpC9Fuhlt00ZEOj6l1V/adS3cSDQ4TjnGgJFoL2xEsTNEe3PiQp09UtIoM4XciDQ9CDQEgSqwkqQPRfoqtEZKdDyEr4bX6Dl1f9LP9CERKAIFIH2xkqQvRao1wfaLtByxCkGX6CrZ9fFP8GuBgKdtUBDz1NGoO1YCbLXAi2duLw4v9W9nr9xtcoT6Pp7jC533sfeFGjeAkWgfZiiQD1/jjKTHoHqsBJkrwWazwP92GYe6EqglxeLO7Or9y8aAl169oZHsnJSaCvNS/jFDV/4/vduf90Xv78h9noegSJQBNobK0H2W6D1O5FudJa97l80BJpdXt2JtLilq9jGINLJogkPVG4Fga5AoDqsBNlvgZb3wtf6QLPTB/Jll5sCLe+Ff7Cz1IZA890g0F4g0BUIVIeVIHsu0BXx3zEchzCN6fT7n//9gzO/8fkNX+CJ9G24J1tTQVYqBIF6WMlhJoiTo3EiTF6g5RcT9ZigFEfrt3L2BYEiUATaGytB9lqgr9yePyrkNP77iSIJfCvnR9+j2Q0CRaAItDdWguy1QLNnVt2RIzdAuRNpBBDoCgSqw0qQ/RZo9uy7lvp8U/cjkvsRFuhLn7tpucOb3tfjnqR5CrR2siFQaQ0CbcNKkD0XaBqCAt1MZ5KfiPfyPW//kb8MgSJQBNobK0EQqIKQQHN/Xnfz0bvfEDDoaw8dItAVCLQEgeqwEgSBKggI9MpBcSvTkqv3i/2uzx8i0BIEWoJAdVgJgkAVBATqjPaXt9rXefkeBFqxFwIdw6AIVIeVIJscPfyJQCXWj7TPER5rv7yA/9v0gZYg0BIEqsNKEASqIGIivTCr/snDdzCIVIFASxCoDitBEKgClUBfXF6+I9AKBFqCQHVYCYJAFYQu4Z1b7hsPFX31vts+VZvG9JmKi3Pk/PnQL4Z56qnixzvfWS144YX8/4PiL/94VUpob2CC851HubHB4497C7761erVE08MybKnAm0fRHry8IPMA90w5Rbo0dFRuWCcFqjcAK03QW1WyA7ZdpALgaPcpwX6rW99q3xFC1TiysHi+i8Wr753hz+N6fli/B2BViDQEgSqA4FOmLaJ9IubbrqpeSvSy/csL+AR6AYEWoJAdSDQCRO8lfOZ8pn2izN31lc8f7jmrd+tr5qlQL2TrXHuWakQBOphJccuBCoe5oubDfxVLQIdZtD9FWh2+uy7ly3Qmx/054Ai0DoItAKB6kCgE0b9ODsu4UsQaAUC1YFAJwwCHQoCrUCgOhDohEGgQ5mkQCuh+QIdZFAEqmMXApUOMwJVgECHgkBLwjciIdBWti9Q+TAjUAV8pcdQEGgJAlWCQCcMAh0KAi1BoEoQ6IRBoENBoCUIVMmWg+THqVWgcf5EoAUIdCgItASBKtmJQIXjjEAVINChINASBKpkBwIVjzMCVRB6GpPq+5MRKAKV1yHQFhDohAk9UNl5Hmg8cxSof7ZNVaDDZ9IjUCUIdAsUj4U/8b9huLGgNxFPpI8HgU5MoEt9jncrEgJVshuBNg90L4Fm4zyOaU8FWvtSuXgQKAKV1yHQFnYhUOlA779AG6QS6LLgGxSdoAgUgcrrEGgLCHQLbFmgL312sbju5qOS9/iPtAuAQIUFViokTqADDIpAlRgTqLBmXwRa+PJ4cdczb1gsimHylUCvHBQX3FcfOFgs3tiz4RgcRHKJ7RBFoMICKxWCQD2s5NiVQEPfnbD/Ar1pbbViwdKf+ZD5ldUj5M/0Gz5HoANBoBUIVMlOBCoc6EkI9FwMwt+5Al3c+Fz+jRt3rQRaTjla/rjluez0s9G2W8FE+oEg0AoEqgSB9mAMgRbfOHycv14uOL1/1Q96Un75cM9uUQQ6EARagUCVbDdIdYCHCDSX54Qv4QtTFsJcLjheCXM98ejKwQ2RQz4FrQI9/UG/kAhUWGClQhCoh5UcOxNoYL7I/gt00+I8WX/r8LIh2rfHsiAs0GfflRf1ys+9L97HCFRYYKVCEKiHlRw7Emhwwt3MBHrzagjeGfYZRaCnD6+KWpZ7fXR5CFRYYKVCEKiHlRwIdBuEBHpj2fn5yu2qe4eCAj1eLK7/Gwev+9Lp31ksovsEZijQ5tmGQAUQaAu2BBo5hrQvAq1G4ZeX8JrHf4QEenmxuLOcvL8a748CgQpLrFQIAvWwkmN3AhXP1NkJdCm7vIl4UjYUL8c3GHNCj7PLCy7vfqrG97tBoMISKxWCQD2s5NiVQANn6vwEuprItGyI5rev92gvFrQ9TKQU6JUDJtIHQaBr2gTqGtRkhewSBLoFWgRaGu7y6k6kxS29ym17nF0p0Phn2yFQYYmVCnFzbB4H2hCo3qAIVMnEBTrIoCYEWk4NXd0L/2C/chHoMCYt0EKelUER6K7YnUDrh7qfQMdpgu7pE+lXQ1KlOeN7VRGosMRKhaQWaKs/EWgLuxKof6gRqILg80BvrAS6lCmDSEEQaAUC1YJAJ0xAoFcOFrc8Vwj06h2L6BmmCFRYZKVCugQ6dBQJgWrZahD38EpnqnT4pXn0CHRFaCJ9fpPoTQdnbn5Dda9oDAhUWGSlQhCoh5UcCHTKBO+F/9OD6tbQ+Kc7IVBhkZUKQaAeVnLsUqC1Y41AFYQfJvLS5/IHN1/X5wviEaiwyEqFIFAPKzl2J9AMgQ6G54EOQjrdEKgAAg2DQCdM+/NAv9+vMAQqLLNSIQjUw0oOBDpl2p8Huii/vC4SBCoss1IhCNTDSo6dCtT9DYEqCD4PdPOA5luiH06CQIVlVioEgXpYybFDgWYIdCgBgeb+PHPzr3/+d9+9NGjsPHoEKi2zUiGxAtUaFIFqsSTQGH8iUIfw80DL2Uunn13wPNAgCHQNAtWCQCdM8F74zezPY27lDIJA1yBQLTsVqPMrAlUQehqTc/smzwMNg0DXIFAtOxRohkAH0vY4O+mXVhCosMxKhSBQDys5thrEP7gagZbmRKA5bU+kL4n/pnkEKiyzUiFNgZbqHOlxTAhUy0QFuv6JQJu434N0En03PAIVllmpEATqYSXHjgW6WYBAFQQE+srti58sm50n8V80j0CFZVYqBIF6WMmxU4FmCHQYvkBPP3pU8O7VPNDfPzpYLG5+D5fwMrJrhBvkDIBAPazksCRQ8dgj0BZ8gS6bnk0YRAqAQDcgUC0IdMIg0CEg0A0IVMuOBbpegkAV8Di7ISDQDQhUy04FmiHQQSDQIUxUoO400NEmgiJQLQh0wiDQISDQDQhUCwKdMGGB/tnn13yBUXgZBLoBgWpBoBMmJNDNd8oxiBQGga4p/giBati1QKtFCFRB+HF2CLQbBLoGgarZYpDWGz8u9vMnAs0JPs7uzIPfX/ODyMIQqLDUSoUgUA8rOSYv0CEG3VOBvnJ79EOUXRCosNRKhexWoI5BDVbIbkGgBrgc/8UbNSIeZxcPAhWWWqkQBOphJcfuBXqhyoFAexO6hEegEQRMg0AFEGiQHQs0Q6DZ2ALNTriEj2DaAl2Lc5RbkRComqkKdIxRpH0VaO07PaJBoMJiKxWCQD2s5ECg2+HquxaLxU135i+PF3dduWNx5n2r+e3PHCzO3DmyQLOrty+uP6rgcXYyCHQDAlWze4FeKHPss0CvlDPbc08eL957UL1e/pJzNLJAH2YeaDch09SWW6mQeIGqDNp+IxICbWHXAq2WXpTX2hLopRiEvzu9v3hE/LMH+aX10pk3PJK3PO/Kr93P3JmdHi/G7gNFoN0g0A0IVA0C7YFWoLVOyeOV0k4WNzy3fJ1/Y9HSr+OOwi/OPBh53e6AQIXlVioEgXpYyYFAt8FSadd/sfplJc1itmYl1pOR54FqxpAQqLTcSoUgUA8rOSwItFi81wJdXVRf/+tFs/B4Ncko//Lhaso7E+l9EGgdBOphJccWg7SfsHEC3fhzWgLNnnlD0R95y3M1gV45SCFQJtJHgUA3IFA1CHRLnP5uPpPp1i20QHt8F7wLAhWWW6kQBOphJQcC3SKnn90MHGV56zNNH2ju5jv7F4ZAheVWKgSBeljJYUKg+Yq9Fmh1qX7loBDoDXlfaCHNNKPwHy2+F56J9K0EPTNRgQ6aSY9A1exeoKsV53uOIU1KoEtD3vBIll29fyXNxY3PlfNArxwsDXr68LjzQL0vN2YeqAgCdUCgahDoNqjuRLr+S7lAj4rXxYX8SYI7kRBoDAjUAYGqQaBb4eoDS4VeV9z/fry4a9n8vK7spXzmDQnuhVeBQIUVVioEgXpYyWFDoBf2XqAOx6qHzYkgUD1TFWj9caCCQDUGRaBqDAg0Q6BKEKieaQt0rc1xhuERqJrJCrR6jUAbvPR9F75UTgSBOiBQNQh02yQXKINIMSBQBwSqBoFuGwQawQ4FWltjpULSCrTrecoINMzWgrQc1gtLzotrwvPopyvQEQlMpP+zz5f82h2LM7/xBSbSSyDQDQhUjwWBZghUR/cgUnHvUxwIVFhjpUIQqIeVHAh0ykSMwsc/WASBCmusVAgC9bCSA4FOmQiBxjdB5yXQ1h6lLeaIBIF6WMmBQKdMhEDjn66MQIVVVioEgXpYyWFDoNkAgQ4w6BwEWj0IqhsEKqyyUiEI1MNKDgQ6ZboFelo+PC8CBCqsslIhCNTDSg4EOmVCzwOtHgV69O6DRfwg0sU5cf68ZtW4O1Lx1FP5/9/5zvWC6uULL6j3WPzF+u+98jc7hSR8+MNRm5WH1Tsy9ZU+jz9e//2rXxV+eeKJqP0L7KlA6xPpmcYksrUW6AXN05Ea9GmB9m+CNlugR84ONnvN7JwhVnKMEOTee++N2aw8qv6Rac3RdiMSLdAYga6eoBcFAhVWIVB3r5mdM8RKDgQ6ZXgakxoE6oBA9SDQCYNA1WxLoBe2JtABo0gIVM+2BFod1CECrfkTgSLQAUxboLUP0fBheASqZ7oCHT6THoG6IFBhFQJ19loPslus5ECgU8YXqDOBaQNfayyBQB0QqB4EOmF8gXpPAuV5oEE6ZiWPluMCAk2DlRzDg9ybTqCtY0gIFIHq2Z5AVd+S2SC9QOs3IiHQWEYQ6NG9v9W9mSNQ0aAIVEFXH+izB4sedyJl+3RadoBAXRCoHgQ6YdoFevrAUp/XPxJbGAIVViJQZ6/1ILvFSg4EOmVaBfrMsvl5Jv5GJAQqrRyY48KEBVr7mCLQEAh0wrQItGfzM0Og4srBAu3aVyTrHI159GPcitQQ6LJIBBrHCALNYkaR1jciZQh0NMICLZqfP9+rMAQqrESg7m4zO2eIlRwIdMqEBHq1d/MzQ6DiSgTq7jazc4ZYyYFAp0xAoIrmZ4ZAxZXDclxAoKmwkmNwkFyeEQLddIHGC7TDn1MS6OnnbuleFP/1RRWiQK/enz8FtGfzM0Og4sqhAu3cVyRJBSrNo0egkYwh0IhRJEegskH3W6CXFzd2LxpHoEXz885+5RQgUGElAnV3m9k5Q6zkQKBbYWsC1TY/MwQqrkSg7m4zO2eIlRwIdCtsS6B/qm1+ZvMSaLthxhLoBQSaDCs5EOgWOM2bhYUvi+HxNz3iLnr2XdU3bwwXKPfCR7ItgUbsLI66QL1PUPkrAt0FExZouWBCAr18UCjtzF2bRQ+XmrsRgbog0DoI1MNKDgTahwsxSH+4ul6/crB40w+y04cXZz5WLbq8OPPg8scz+aLhAuV5oJEgUBcEOgAE2oOBAj0puz2Pi9Zo8cvx6mFJywbpXWNNY9KCQIXVQ3Jc2LJA1TfDiwKt7wSBhhgYpJgCmkagvj8NCFRNYcvCkqvfbnjOGUR66c9+7Y4FAq2xHwKN21sM/QTac48IdABjCDTCoJsbkUYXqN6gWxboK7fnl+5Z2dlZCvTqHWVXJQJ1QaB1tivQ5scUgYbYgUBFg85CoKUgHYFeyecdvfG9X+QSvg4CrYNAPazkQKBbIdQCXV7V35iP8dAH6rEHAr2AQBNiJceWBOp2gWoF2vDn1AQq9IFWTn3ldgRaYy8EGrm3GBCoh5UcCHQrhEbhK4Fezr+6CIFuQKB1EKiHlRwIdCsUbc5yHuhLD5TzQJeLVpfwp59dINA6CLQOAvWwkgOBboV8rCi/bF/fiVQturwagr/hs8v2KALdkDZIl1/GEOiFvRLo2qBWzhArOQYGKZ8EmkKg3WNIExJo/pSPvA26vhd+vejZOxaL6+4s2qMIdMM+CDR6dxEgUA8rOUYS6NFvtRt09gJNAwLVgUBrINAB7EKgkkERqAIEqgOB1pAFKneCWjlDrORAoFMGgerYgkAvINCkWMmBQKcMAtWxFYH22F83VQ7Jn8LjmPrtEYEOYBSBdo4i1W5EGk2gQ58mgkBdEKiwfpYClZo5CDQAAp0wCFQHAq2BQAeAQCcMAtXRqZf1BtocFxBoWqzkGBZk/YXwHQL1ukBjBBrjTwQ6ZmEIVNhAL9Be++sEgXpYyTGWQNtHkRBoGhCoDgTqstoWgerYiUAFgyJQBQhUBwJ1CQzCI9AoEOiEQaAquu0yVKAXdiJQ5URQBDoEBDphEKiKbQi05w67QKAeVnIg0CmDQFUg0BoIdAgIdMIgUBUItEZIoLX9INAACHTCIFAVyQV6AYGmxkqOQUHW/hxfoA1/IlABBKpiCwLtvccO+gq0zx4R6BDGEWiHQb0bkRDoSCBQFQi0BgIdAgKdMAhUBQKtEbgRCYFGsRuBNs8BBKoAgapILVC/CxSBjo+VHFsRaKMLVCFQyZ8IdMzCEKiwhVKg/ffYgStQ4UboQbciIdAhTFqgAw2KQF0QqLAFAnV27AbZNVZyINApg0BVINAaCHQICHTCIFANMW6pttHkaHaBItDRsZJjSBDHn2MLNG4MCYGOWRgCFbbRCVS1y1YQqIeVHOMJNAsbFIEmAoFqQKB1ggJ194RAAyDQCYNANSDQOgh0CDsSaOMsQKAKEKiGxAIVukAR6OhYyYFApwwC1ZBcoMp9tlHmkOfRSwLtsUcEOoSRBNo6itS8EQmBjgIC1YBA60QJtDKolTPESg4EOmUQqAYEWscTaGCkAoHKINAJg0A1pBWo1AW6LYHqOkER6BD0QWr+bBOo1AXaLtBYfyLQEUGgwjYagWr32QYC9bCSY0SBBkeREGgqEKgGBFoHgQ4BgU4YBKoBgQrREKiSXQnUPw0QqAIEqiDKLGqByl2gCHRsrORAoFMGgSpILVD9TltwBCr4c9BMegQ6iGkLdLUcgY4BAhU2mrdAhZn0Vs4QKzkQ6JRBoAoQqBQNgSpBoBMGgSqIM0u5Vd8cgS5QBDo2VnLog9T9OapAm/5EoCIIVEFigQ7aa5D+Ao3eIwIdxFgCbTGodCMSAh0DBKoAgUrJEKgSBDphEKgCBColQ6BKEOiEQaAKUgo01AVqXqCyPxFoN+kFKneB+r+3CzTkTwQ6HghU2Kq3QIftNQgC9bCSA4FOGQSqAIEKyRCoFgQ6YRCoAgQqJEOgWhDohEGg/Yn0ikqg4S5QBDoyVnKog/j+HFGgPcaQEOh4IFBhs74CHbrbEDsSqPMbApUZU6CyQdMLVGtQBOqCQIXNEGj1CoHKINAJg0D7g0ClZAhUCwKdMAi0PwkF2tIFuiWBam6GR6CD2J1A6wsQqAKVQP/iVw4Pb/vAdxrLEaiwWU+BDt9vgFWOoD+HNEER6CBGE2hwFEm+EclfohTooFGkWQr024cFt33KX4FAhc0QaPUKgcog0AmjEOiLh7f9apb9+KHDt37XW4NAhc0QaPUKgcog0AnTX6CvPXT4kfznq/etfjogUGG7PjnaukAR6MhYyaEN0vRnSKDBLtCQQHv5E4H249X7ypbnk4cf9FYhUGG7fgIdY78yWxNo8JE/CFRmPyO3HgAAIABJREFUVIFKBkWg6RgwCo9Ao7ZDoNUrBCqDQCeMXqCv3tcYRUor0Asyoc0RaB0E6mElBwKdMnqBPn/4jurlZyou9uL8+X5bi/zhH/bb6XCeeipyw37vryi5teje5Ym7uHjxne8UV5WLX3ih7x5XW63/riznZ36m9uvFHhW3p4xx/Bw+/OH1yxfKys/r+u///eCOy0Pxsz/7s9WawKnw+OPrl5/+9OrnV78aDFKseuKJuNQe8xXoiyNMY+rVpAps/D9//evy9skaGE8/fal7o5zeLdCOkq22QL0H0pflvP/976/9nlVNUCstv63nCF0uDW+BXiorP64Fevfdd69XueeC2AI9d+5c8ZMWqIxWoC/ec5s/Bo9APRCo+3uGQBFog7kK9Hmh/YlAPRCo+3uGQBFog5kK9NuiP3ch0Gt7JNCnlyW3FY1AR2XiAq37c1X7UQK9+27HoKJA3TEkBNqORqCvPXn4Nv8mpAIEWqO/QLNWgQ4zqEagcXtEoHEkFGjW2gSt34i0tCcCHQ2NQJ88fPuPxBUItAYCdX/PECgCbTBHgT4f8icCrYNA3d8zBIpAG8xQoK/ed1jxDm9V74n0fYwgbntt7wSarBN0BwJ9v/t7hkATCfRSIdBwJ2ijC9QZRooVaIs/EWg/XjzciUBDDdA/zgIGTfX56Gwnrukp0Kdzv2xBoAF/Vh+m3gItt6n+bu3PDIHWWNaTXJ3DBVr9L06g1f+chfUctUH4rDBom0CLlQh0BBBojd4CzRDoFkGgm4X1HAg0HgTaFwQqp0KgHSBQAQTqsnWBXjMt0FXmvgJtKXsrAu09EbRLoJsdItDxBFrvAq3+3y3Qu7sFWu8CRaCtTF2gf7w3An16pRcEui32R6CbH0VV+wb1BJplmTyKhEAVINC+pBNo8QOBbos5CzRDoCOBQPuCQOVUCLQDBCqAQF36P1A5XgnSltf2UqDhwicm0MYoEgJNINBLa4FeihDo3Qh0XKYi0HADNAsYNNHno3usfINGoMmaoBernfQRaMQeqy3qAi3ciUAdinoSq3OoQN0X0iiSeyNSJU6pE1QQaCHP/H+dAlUaFIG6INA6fQT69MQF6t2IhEB9bAlUaoIiUAUItCfpBFq+QKBbAoFuFtdyINAeTFmg1/ZUoGk6QRGox6QF2ugCdTpBPYFKXaBtAvW6QIv/tfoTgY7F1gWa7aFA2wpHoCOyLwJdLwuMItUFWi1dqxSBDgKB9iSRQJ/eF4E6g/AI1MWYQDMEOgoItCfJBLp+iUC3AwJdL67lQKB92LFAo5UgbHdtbwUaLH1iAvUNikBHF+ilmkAvdQj0bgQ6NpMWaHWsRYOm+XxE3C+0QSfQlsLHEGjQn7rHMcnTQBGoT1lPUn0OE6izMEKgm8WVTNsEWj6N/ty5doEOmEmPQF3SCbTtCn4fBPr0ngm0NCcCXWNOoM0mKAJVgED7kUygzi8IdCsg0PXyWg4E2ofpCvSafYEWsTUCDZWOQEdkygKVukCL31oFeneMQBtdoAi0jSkLNNtTgaZpguoE2rlHBBpHKoHWFotN0M2NSDV/NjtBEagCBNoPBBpIhEDbsSfQDIGOwK4FGqmEPRfo03sq0PInAkWgARCoyzYFem2/BFr7NUknaLRA+3WC9hHo6n0i0OFBSoH6J4rUCRrqAu0j0I+3+xOBjsR2Beoca8mgST4fUc9M2qAUaLh0cwJdr0agrazrSajPIQL1lncItL5xKdSwQEt/ZtnHP94eB4GOQzKBtl/BI9AYtiPQuj8RaIVFgfpNUASqAIH2Io1An95zgdY7QRHo8CAI1ApTFei1/RKotyDYCYpAx2PCAg10geZLvhkU6E9FCVToAkWgLUxXoNkeCzRYPAIdj30QaGPNSqCuQR2B+hsj0OEg0F4g0FAgBNqKUYHWRpEQqIKdCzTKCfst0EYX6P4IVJoIikAHBxlJoBkCHcxEBXptvwTaWBTqBEWg4zF5gUonyaVvfjP/IQj0pxBoCiYr0Kx2rAWDpvh8xD03foNaoAmaoBezLn8qbkXarESgbTiV2KxPvUCFVY1RpOpGJMGfHQJd+zP71sfPZa0sDYpAh4NAPRAoAi0wK1DXoAhUwSQE2tUFikAj2KJAN/5EoCuMCjRDoEOZpkCvTUSgefLOHMIYUopOUATqMV2BhrtAq07QjUDbukAFgYpdoN/61jkEGmCqAs3/vzcClZYi0NRMX6DiSm8UaSNQaeMRBaozKAJ1QaA+CBSB5tgVaG5QBKpn9wKNcAICdQtUshWB+mNInkERqDGBZgh0IJMU6LV9EqjYBRrsBEWgozFxgYZ6yb/5zXy5L9C7EWgaJirQ/P/usRYMmuDzEfndmQ5RApWXT0GgzjoE2kKtDhsVqhWovPIoIFD5uLcI1BmE/1bWYVAEOgoI1AeBItDMtkDvRqBDmIJAu7tAEWg3aQUaGENCoAV2Beo2QRGogikKtNkFup8ClXawDYHG3wyPQOMYV6CtXaC5QC81BHp3pEDFLlAE2sI0BVr82A+BBsaQgjtQG1Qr0LY9ItA4kgg0tLreBC0FGvInAh2IAYF2OmHPBRpasx8CXf2GQAO/aIKMLNC7EegAEGgPEGgwDAJtw7BAMwQ6iAkK9NpsBDpqJygC9Zi0QINdoF4naFcXaIxAczsi0ACTFGjxo+ZPwaDjfz4818UYtFOg4S7QwA4GCrTVn32H4RuzmBCoiFeDfoXqBBpcfXQkCDR83EMCrTVAYwyKQAeDQH0iBBpeh0CTgkCz1ToEOoAJCDSmCxSBdrI1gdb9WRuGR6DCrzsXaGVQBKpgegIVu0CtCnQZfohAhT2YFqjQAEWgYwu0owu0EOilmkDvbhNo5gg0OIaEQENMUaCrn3sh0LYuUHkPCHQspi3Q8PplVW+aoKVA28YOEegQLAi0Qwr7LdC2tXshULcTFIEKvyLQKYNA40Gg4SxxAi3eLwK1JtC7EaiayQn02qwE2twFAh2LCQu0rQt0JdBLjkDvbhdohkAHMEGBrn56/mwadPTPR0N2ccPwAwQ6ahP0Yvc8elmgwT26yxFomEb9eQs0Am3bYNUEdQXafthFgTrCLATaYVAEOgYItEG7QNvHkBBoUhBoCQIdAAKNJolA2/8agSYEgZZUl/gIVIF9gcZ1ge6rQMfrBN2WQH1/ItBxBdrVBVp1ghYCjekCXRv2YksXKAINMD2Bli9mIVBpFwh0JKYs0NYt8roum6BlAxSBJsOEQFulsMcC7eoC3Q+B+hNBESgCXYNAXRBogw6Bdv05Ak0HAq0ob/VEoAomJtBrsxNoYx9bEGjkRNCGQMUxJAQ6tkA7ukBLgV4qBXp3t0AzBKpmcgItXzT82TDo2J8PwXYRBh0oUGEXgwTa8UHqNZO+thiBBhGqr76ov0Dbtyiq2hFophBoYxA+kUERqAsCbdAm0O4uUASaEAS6BoGqQaCxpBBo914RaDIQ6JrVV34gUAXmBRrbBWpUoNmF831KFHbh78O4QJv+RKAdi3oF6e4CrQT6jUKgd8cIdNUEfVzsAkWgrUxNoNWruQh0vCaoXqCBPSLQOMYXaNc2myZoLtCs258IVI0NgcY98QeBrkqcmkCLZQg0uAiBThgEGsvoAo0ZQ0Kg6UCgG4pOUASqYFICvbZnAo3Z7WidoAjUY6IC/b1ogX7jt2K7QJcGDQq08icCFZmYQKtXgj99g478+RB1FzMMP1CgzX0YEWh9IQIN0V15fQXauU15EFcC3SxowxOo0ADtMCgCHQEE2gSBuqNICFRYiEAnDAKNZHSBxnWBItBkIFAHBKpkSgJt7wKdnkDjdtzoBEWg4zBJgcZ0ga4F+o0L/yBaoHf/1G8/vvkVgcZiRKBRD6zY+HNOAm3sxKJAW/3pdIIiUGFhT4F2b1Q9EuYbv/UP7nYXtIFAdSDQSBCoBwKNA4G2gUBdEGgTBJoh0PaFCHTCTEig1/ZKoLFjSM1OUAQ6DpMU6O/1EujX+wj0UQSqYFICXb8U/ekZdNzPR0B33QYNCzR63+M0QS/G+HM9Cbtzjz3GkGYt0IgnsfQTaMxW5XFeCtT9tR1XoK4qNwLtMigCHQoCbYJAMwTavhiBThgEGscAgV4Q+frX725yTeT/Q6ApCOSQD5fyGYJDBPp7MuK23hnzrW8VP77+9a9fyvnmNy918+ijX/5Wxcc//i2H9V4QaJNxBXpRzfnzXcv/6q/+ar308cfFjf/wD/UBOnjqKXHxCy90/eF5ma985Ss/2+Cnf/qn/0rgL//yL70ite/hne/0Fn3605/2lhSbvOC9L2GP9UXF9vmf/szPVIs+8YlPrFfnS4uCA9U4Dh/+8IeHF7I8MIFDpivP+bsvf/nL0uIA/1zid37n/5C29c+Yr361+PGv/tX//kLOv/yXL3Tz5S9/dc0//Ifrl//0n24OY+NkcfnqV594IqZC6iBQlwEt0O4HVjgN0Cm1QAM5Hn1UaAyefctbpG2P/t2/q/0+Ygv0XKNRETuKJLdAnQbo2bNnN+vdJmiyFui9997bZ3M5x2OPPSou17ZBnb979NFHpcWBIGJbs1apG9zPRk55nKujG3UJ7+Zwzgp3j7RAmyDQOBCoBwKNA4G2gkBdEKgAAs0QaAECbYJAXVIK9NpeCXT5WWqe1WeXAhUMerQUaM2g4wn03LmGQSuBdn0rUpdAz551P3rbEOi99/YzqJjjsaVARYNqx5E2f/boo45BOwUqDhfVK3XNNVmg66M7QKC1PSLQJhMS6GZpwJ91g476OQ1OOuo2aECgwlm9PFNlgWbpBNr4UFSTsDsE6i3JN68Pwi/fzNYFmo0h0EwWqLLWaw3QTG6CBgQqLKxX6hrfn2uBZjqB1hqgWWQTFIEOB4EKINAMgeYgUAEE6oJABRBohkBzEKgAAnVJKNCYLtDpCPTRsEAbBj0qBOoadDSBnis+YvUPxVqg7hvrL9CzxSdv/dHbgkDvLQTaw6BSjscKgY7YCVrrAs3kTlApiDxjvl6pFY0u0FVVb46uXqDeYUSgDcwIVNSC3ADdB4GuxFijOFElgS7/N0ITVBRo1vhQVJt0NEFlgdYaoJnXBE0u0Op/kcgCzbJRm6CuQGtFdwpUWFicIZJAG1vmdb0+upH+lAWauXtsF6jmaSII1AWBCoQE2jivi/MUgWqZhUAzBGoMBBoFAvVKQ6BxINB2EKgLAhUQcjzaJlDPoEelQAd3gjYFeq78iNU+FRuBtnaCRgrU7QTdjkDjDSrkeKwUaKATVJFq/UePlgJ91F8hBgl2gQpz6ZtdoMWx2BxdvUDPentEoA0mItC4MaSaQcf8nLY8e67ToKJA8/97naDladoUaPFjFIF+TWiAxjVB/T36v/sCLd9McxQpmUBLdQ4VaPFjvFEkrwEqN0FFgQqFNSp1heDPlUCLVwqBeg3Q+CYoAh1ISoE6S/dEoJkv0OLHvgjU/ZFtRaDujxj6ClRT7SML1P2xBoHuEgQaAwJFoAg0Q6BN7Ai09YbrvRLoo+0CrRn0aC1Qx6DjCPTc+iPW3QmqFqjfCZpeoNEGbeZ4bC3QsTpBvS5QuRO0GaSlC7TRCSp1gS6r2jm6aoGebewRgfpMQ6DX9kygq5+iQDNfoOWLFAL1X9T3OECg60/c1gS6FucwgZYvRusEdQTqv2gXqFRYs1JzJH8WAi1fDhKo/wKB+pgWqNwA3SOBOmf22akLVGiAblWg/otOegtUUe9jClT4VykHge4UBBpDGoFmdYGWL/ZMoNUrBOq9UAjUf1GAQHcKAo1hVIE+2iVQx6BHjkA3Bh1FoOecj5j7sXAEunlrWoFmOxBorEEbOR5zBDpSJ2j1F486An3UL6wRpLUL1OsEFbtAs6MPOUf3Q0qBnm3uEYH6TEKg0V2grkFH/Jy2fgVxl0EFgVavRIFmdYGuXw5tgl705tE7nwVZoC1N0Mb+6wI9u3WBOtocItD1y5E6QZsNUKkJKghUKkyo1CzQAM2yD31IetlOmaPZAI1rgiLQwaQTqLt0nwS6ltXZ/RLoesXWBCq9bKe/QHvX+5gClf5VyhDorkGgEaQSaOYKdL1wzwRavkag/sveApVeItAdY0ig4Ruu90qgj3YL9C2b9a5Ah3WCegI9VxOo87FwBRrqBI0XqN8E3YZAIw3q53isJtBROkGbXaBSJ6gfpKMLtNYYlbtAs3M1gbZ9lZGDJ9Cz0h4RqMcUBHptzwS6eS0KNHMF6vzh2ALN5F+cjZQCPbt1gdakqReo88s4naAbgQpFtwhUKkus1FAD1B04OlILVNg7AvWwLFC5AYpA7Ql0NY9eaoBuS6ChX1pQCLRvxSPQLhCoCwIVaBdoeZ6f3WOB1jpBEahSoHKzHoHuGgQawZgCrfeubQTqLFx3gh55Al0bdLhAz3kC3fxWE+j6vakFmu1AoHEG9XI85gl0jE7QcvNHPYE+Wi/LC9LZBerYNNQF6gk0zqB1gZ4V94hAPaYn0FZ/OgYd73Pa6s9OgzYE6v4mCjTbCLT2p0MF+rVQAzSiCVrbo9QFakegsU3QhkBrv44hULEB2myCNgQqlSVXaqgB6t595NyV1M7F8m+lHUY1QR9DoMNII9A+Y0gINJxjWwI9u3WB3rtFgfapeQTaCQJ1SSXQ2tI9E2hxop+dvkDdQfjauuq9JRVo268hNALtV/MjCjTwrxIC3TWWBOqfnfsoUL9vrRJobWHZCep/8fGmE3SwQP1vg+/sBNUL1GmCrmbSb0GgUQat53isIdDh1/BiF2izE7QeJKILdO3TYBeoL9Aog9YE6n/1UkwnKAIdCAIV8AVaXysKNKsE6hU1qAnqCdRbKws00AQ1LtDIJqgvUG/1mAKViw4JVCpKrtRgA9Q5ikdHrdZz8AQqJ0CgdewL1P9XFoFmpgXa+NbI5AJttDiTCrRHzSPQThCoy7gClRugeyfQI8E5kxRooAHqdIImE2jXAhGVQHvV/HgCDfyrhEB3DgLtZkSBNnvWVgL1FhadoH4X6MBOUFegzU4xZ0ldoFInaD+BZmuBrkaRtiHQGIPWcvhdoKN0gq62bRZULhEFGtUFWho13AXaEGiMQV2BNoxdLUGgdSYn0A5/bgw62ue0w59dBvUE6q8WBZqtBNooazyBNlbLAs0kgTZ3bkygcU1QT6CN9YMFGmqA+k1QT6BSUXKltjRA6wKNa4JeXP+xtMPuJqhmJj0CdUkh0J5doAg0mGM7Am02XVILVGhvJhVodNUj0G4QqEsagda3QaA5lgXaKLp0ajqBxixqohNoj6pHoN0gUJehApVmG+6VQIV+taOjI6HR9pa3vKXZBep0gg4TqNQltlnmCfRSY499Bbppgm5PoBEGdXM0u0BH6AQNdYH6naBukFAXqCDQs21doA2BRhjUEaiww+5OUAQ6FAQqUBdoc30u0ObSXKBCYWMJVFgvCzSbpECjmqB1gQobjCXQYNGiQKWS5Epta4B6Ao1qgtYEGkyBQGtYF2jjX1kEmmNWoFLTJa1AxdZmUoHG1j0C7QaBuowqULkBikALzAm0rQG66QRNI9DYhR5KgcbXPQLtBoG6IFABJ4fYq3b05jdLAn3962WBrgw6SKByh9h6qS/QS/4eews024FAuw3q5JC6QId3ghYbyoWsljYFGt0Fulz4wx+2dIE2Bdpt0I1AxR2WSxFojakJtNOfa4OO9Tnt9GeHQWsClTZ485ulpa9/vViavgn6VHsDNL4JKuzanEBjmqA1gYpbDBNoSwO03gStCVTaWK7UH/5QLNn7d/DoqL64hYtZawM0ognafyY9AnUZX6D9u0ARaICtCFRuuqQUaKCtiUAR6FawJdDGhWL/K3gEGmBLAhWLdjpBxxdon8UueoHG1T0CjQCBuiBQAQSauU3Q6Qs0tu4RaAQI1AWBCmxyyOMJZ9/8ZmG46Oj1r39Lc+mQUaSn2seQ4keRJiLQToNucshjSENHkVrGkOqjSJsgPcaQsms//KG02D+KRx1H3WEt0EBHTPcoEgIdBgIVcAUqrT97VpqwdHT0FlGg2RgCDWwhCzQbR6D5ii0KNKIJ6go0sMkYAm0ruilQaVO5Uq9dE5f71xFHHdcdDo5AA1sg0AZmBSqPISFQqwL9iZYxpKz86CUQaLClmVSgUZWPQCNAoC4JBOpvgEBtCzS093QC7btijVqgkZWPQCNAoC7pBRrhz8qgI31OI/zZbtB1jkAXaC7QhkGPcoG2dYLqBRruDKvWNAR6yd2jtGeTAu0y6DpHqAt0WCdoaxdorRN0HaRXF2gu0OaKxjNhHIF2GfRiRxdoZycoAh0IAhVwBCqtzk9JSaCbLzf2GC7Q4CayQLNxBHp2uwLtboI6Ag1uM1yg7UX7ApU2DDVAxTWNpxJuDmYPgQY3QaA+xgRav1BUdYEiUJktCDTcdKmaoD8xskBb2plJBRpR+wg0BgTqMr5A/dUI1KZAv1kMwrd88pIJVLNqhV6gUbWPQGNAoC4IVKDKEegOEwV6tBLoqJ2gpUDbusL851CU1DtBpyPQDoNWOcJdoIM6QfNtwl2g5bqaQOUuULlWr60E6q9yj25xGJ1j2dkJWgq0/TriLAKtgUA7GFGg0tqzK4F6ylr9Pm4TdC3Qlm1kgWbjCPTsdgXa2QTdCLRlo6EC7SjaE6i0WWgMabXOW+weXV+gnU3QtUBbtkGgHpYFKnxlwR4KNP//fgi0remyeqfvf//R18YUaGsrM6lAu2t/PIFKSxGoEawKVG6AItDMokDXY0ht+08jUO3KbJBAY2ofgcaAQF2SCzTKn6VBx/mcRvmz1aBljpYu0IayjiqBtnSC9hboH3wt/397R1hrJ+hqj62D8G3734FA2w1a5mjrAh3QCdrVBep0gpZBeneBNi/vz3UItN2gFzu7QLs6QVurUgSBuiBQgbVApZVnK4HWlFX91tYEVQu0daO2JuhggZ7drkC7mqBrgbZuNUygnUXXBCpt1NYF2qjy2tFtCLTr4F/sboB2NEER6DBGEKhzoajtAp2WQFc/90Gg7U2Xsgn6E0dPPxUZuJuONmZSgXZV/3gClZYiUCuYFmhjJQLNMSvQ9gApBDpk9RCBdlc/Ao0CgbogUIFVjtYuUO88P9oINNwJqhNoVzfYar0g0EuTFGjr+tWBae8C1XeCLjdo7wJdFb0RqKIL1L/AP9cp0NajXwi0+zri7JijSAjUBYEKVAKV1p3dCNQ50TevW5qgWoF2bCULNBtHoGe3K9CO9ZVAO3YyRKDdRbsClTZp7wL1/Fo/uk2Bdhz+UqDtmTMEWgOBtpNaoNWrGQg0Q6DNorsFKi1FoGYwKlB5DAmBrjAn0G9GjCGNL9DOmzWTCrSj/hFoFAjUZWSBNtZF+nNl0FEEGunPNoMWOTq6QGsn+pEr0NE6QZ/+g6OYZ0IWW0gCLQ0q7rUUaFeE8QXauUHbFsWB6eoC1XaCRnSBrjtBiyCqLtD6Jb5/dJeH0TuS7cf/YkQXaHsnKAIdxhgCXZ2Z+ydQcV1AoJsNxmuClgLt3E4WaDaOQHODfm17Am3fohRo5270Ao0peiNQcYuOLtDaBv7RbQq0/QS4GNMAbW2CItBhIFCBFoGedQV6tHm52WKvBJoh0GbR7QLtGkNCoLvGrEAHdIFOSKCb11MXaPe139gCjfjm4qQCba3/0QQqLUWgdjAs0OaqyQq0swvUkdZRXaDBTlCFQCO+G7zYRhTopS6BdmcYW6ARm7Rskx+Y7i5QZSfohQvdXaBF0aVAlV2gbhu1cXRFgbacAUuBxv0zeBaBrkGgrYwmUHFVQKDuJuEmqEqgERvKAs3GEejZ7Qq0dZuVQCN2pBVoVNFrgYrrO6/gnU0aR1cQaOspUAi0Le16jwh0DQJtJalAz9YFelS9cLfZK4FmCLRRdKtAu7tAEeiOsSrQfzMLgbq/TVugb9m6QCO6QBMLtO0AjCVQaSkCNYRNgQ5sgBYGHUOg0f5sMejFqC5Q50vAPIGGOkF7CvRrfxDVBdrWCbrco7zTS7/9E1GfvJEFGrVReKuLcV2gqk7QuC7QshP04oAu0E0rtXl0ZYGGz4FPR3WBtnaC9n6aCAJ1GUWgWfE53TOBimsCAq1vFGyCagQatWWwCTqGQM/+xO9tU6BtW12M/rzrBBpZdClQcW3EFfx6o+bRlQTadhJ8Oq4B2tYERaCDQKACCNQBgTaKRqCTxqhAh3WBTkOgZ32BHq1+1LeahkBfv3WBRnWBJhZo+AiMI9CYMSQEulvMClRYMVWBRnWBrh/+0BDoOJ2gX/uDuC7QoptMFujSoPI+v/nbb4n75C3f8+tHE2jkZsHtLkZ2gWo6QS88Fi3Qx5YCHdAFWmlWOrqNkymwXcmnfzNaoL+JQEsQaBtjCVRcERCov9lITdBcoJGbygLNxhBodvYtv/cHkZu2EynQlu0uxn/cNQKNLXolUHFdVAO03Ew6upJAW5qgS4EGY3p7RKAVCLQNBOqAQD0QaAMEOggEKhASaKODqzjdEWgkMxBoXBcoAt0pJgU6eAwpN+gIAu3hz7BBL0Z2gZad/oJAA52gf9JPoP9LD4F+SBbo0wFl/PY/iuw8ywX6z0YRaOQYUtuGF2O7QEOdoOFRpOgu0KIT9PygLtDqCZ3xAg2cB+f+XvxRDBkUgUbx2ifvOTz8wHcay8cRaHZhcAPUmEDF5QGBNjcMNUF7CzR6W1mgWZtAo8s++8/+efS2LcT6s2XLiz0+7T2boPEN0LwJen7YFXz4+UiiQINN0KVAQxmbe0SgJRqBvnrfYc5bv+uvQKACCLQGAvWLRqBTRiPQJw/f/p3sxw8dvv1H3goEKoBAayBQv2gEOmUUAn35nqLt+ep9t33KWzOWQP/NPgn0y5FjSMX5LgtU7gT9kz/pkS77X3sJ9EPS4ktPPy0b4x/1EujvRG8bJroLtGXTr/QSaK9O0Md6CfQrPcaQpOeMF12SfQQqnwm/2UugvykuR6ARPH/4jvLnB70OWcw9AAAIMklEQVQ14wlUWjxZgYqLpU+HfMKHmqB9BdpjY1mgWVigPcoeSaDDt/1Knw97vyZoL48sBSouj26ABhuEgfMpKFBxsQwCLVEI9MnDjxQ/XyxFugGBCiDQOgjU3xiBTpj+An3tofLS/eV7/E7QvgL91zJ/9Ed/9D+J/Gfx/NE4/K14Am9myW/KnGvwoQ99qLnw3Ln/XKbve/kP4gm8xWUhgbf4P8Yz0pH5f6MJlxE4MBLBM3V40cut/zsZsf6kM2RZylmBN7/5zdLiUJB+ApXfCwLtRBDoZyou9iMo0H8t+vM/6cE4n9Ie/mwR6N+T+XSDX/qlX2ou/PSn/1OZnu/lv/jpeIICDbzDf/zfxzPOkfmj/yeacBmBAyPRU6ChYy6x3Pwfy4j1J50hy1I+IfDX/pq09BOhIJ/o8dENldHTABfnLVB/ItNIl/DjYCUIOTysBLGSw0yQXeSYt0CHXsInxUoQcnhYCWIlh5kgCFQBAk0NOTysBLGSw0wQBKrA4ij8OFgJQg4PK0Gs5DATBIEqUM0D/WDt5wYEKkAODytBrOQwEwSBKrB4J9I4WAlCDg8rQazkMBMEgSpQCPS1hw7flvRe+HGwEoQcHlaCWMlhJggCVaB5mMiPEz+NaRysBCGHh5UgVnKYCYJAFaieB/rjTy79+QG//YlARcjhYSWIlRxmgiBQBQafSD8SVoKQw8NKECs5zARBoAoQaGrI4WEliJUcZoIgUAUINDXk8LASxEoOM0EQqAIEmhpyeFgJYiWHmSAIVAECTQ05PKwEsZLDTBAEqgCBpoYcHlaCWMlhJggCVYBAU0MODytBrOQwEwSBKkCgqSGHh5UgVnKYCYJAFSDQ1JDDw0oQKznMBEGgChBoasjhYSWIlRxmgiBQBQg0NeTwsBLESg4zQRCoAgSaGnJ4WAliJYeZIAhUAQJNDTk8rASxksNMEASqAIGmhhweVoJYyWEmCAJVgEBTQw4PK0Gs5DATBIEqQKCpIYeHlSBWcpgJgkAVINDUkMPDShArOcwEQaAKEGhqyOFhJYiVHGaCIFAFCDQ15PCwEsRKDjNBEKgCBJoacnhYCWIlh5kgCFQBAk0NOTysBLGSw0wQBKoAgaaGHB5WgljJYSYIAlWAQFNDDg8rQazkMBMEgSpAoKkhh4eVIFZymAmCQBUg0NSQw8NKECs5zARBoArGFSgAQB/GFNAOQKAAsDvGFNAOGFWgOVOvkNGhQjyoEA8qZMIg0NRQIR5UiAcVMmEQaGqoEA8qxIMKmTAINDVUiAcV4kGFTBgEmhoqxIMK8aBCJgwCTQ0V4kGFeFAhEwaBpoYK8aBCPKiQCTO6QAEA5gICBQBQgkABAJQgUAAAJQgUAEAJAgUAUIJAAQCUIFAAACV6gf7Frxwe3vaB76x+ee2T9xweVr8sefW+d5SvfrzczF2zv/SokNv+yx9tP9/WCVdIa1XtL5EVkrmnC9hGLdBvHxbc9qn8l1fvK35563ertU8elifAy/c4m+01kRXy56vN3vZduZQ9Ilwh7VW1t0RWSM76dAHjaAX64uFtv7psTT20OgOePHz7d/Jf3r5qWL325GF5Arz2UH3N/hJZIct/UJZrXvv2nCuktar2l9gKcU8XsI5SoEsxfiT/ufx39CO5FYoj/+p9q39C/+KXDze+qK3ZX2Ir5MlSFE+uNt9fwhXSWlX7S2yF1E4XsI5SoK/eV/5j+eThB7Ps+fJwP5//svz/4S/+ebnkxdXP6hTZXyIrZF0RL+77JyRcIW1VtcfEVkjt8wPWGToKXxzzJ2tWeP5t/2Tth9m0L9a0V8hrD5UV8fI9e3/JWiJUSOeavaajQmqfH7DOQIGWlyANK7zo94HO5XzoqJD5CTRUIW1r9pqOClmBQKfCQIEWVyItAs2HS3J+cR4fju4KedLrC917QhXStmav6aiQFQh0KgwT6IvFzAvnbKgmo6xPgJd/eTVrZw7T/LKICnn5nvwfk/yflRnM28laKqRlzV7TVSHVawQ6DQYJ9MV7bst7cloaXGtfzKMPtLtC8iGCnL89jwZXS4UE1+w1nRVS/oJAJ8IQgT5farH1ivWD5c85nBARFbKapPIL35mHL8IV0lJV+0x3haxAoFNhgEA3zcrmkOIsx0wiKmTNLD4h4Qppq6o9JqZCaovBOmqBvvbk5nbEag7fZi7fDAUaVSFrntz3aY9tFdJeVXtLXIUUINCpoBaoO4zcnO05w0v4uAopPzgv37P/ncLhCmmvqr0lrkIKEOhU0Ar0efeQv/ZQPszu3s9cnQAvHs5lECm6QvLbnv/inv1vb4UrpKOq9pXICilAoFNBfSvnYUV+pH/sP1HHH3Tee19EV0j54J2910W4Qrqqak+JrpAcBDoVlAJ98bB+zH/8yeWrD/zIWV+dAP82fx7oL+z9NND4Cvnzv3l4+Nd/dRcZt0q4Qrqqak+Jr5AMgU4HnkgPAKAEgQIAKEGgAABKECgAgBIECgCgBIECAChBoAAAShAoAIASBAoAoASBAgAoQaAAAEoQKACAEgQKAKAEgQIAKEGgAABKECgAgBIECgCgBIECAChBoAAAShAoAIASBAoAoASBAgAoQaAAAEoQKACAEgQKAKAEgQIAKEGgAABKECgAgBIECgCgBIECAChBoAAAShAoAIASBAoAoASBAgAoQaAAAEoQKACAEgQKAKAEgQIAKEGgAABKECgAgBIECgCgBIECAChBoAAAShAoAIASBAoAoASBAgAoQaAAAEoQKACAEgQKAKAEgQIAKEGgAABKECgAgBIECgCgBIECACj5/wHbSupo6qLP+AAAAABJRU5ErkJggg==" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="appendix-a15-sensitivity-checks-leaving-out-1-topic-at-the-time" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a15-sensitivity-checks-leaving-out-1-topic-at-the-time">Appendix A15: sensitivity checks: leaving out 1 topic at the time</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>cns <span class="ot">=</span> <span class="fu">unique</span>(dc<span class="sc">$</span>cn)</span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>res2 <span class="ot">=</span>  <span class="fu">lapply</span>(cns,<span class="cf">function</span>(x){</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>  dcn <span class="ot">=</span> dc <span class="sc">|&gt;</span> <span class="fu">filter</span>(cn <span class="sc">==</span> x)</span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>  tops <span class="ot">=</span> <span class="fu">unique</span>(dcn<span class="sc">$</span>topic)</span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">lapply</span>(tops,<span class="cf">function</span>(x){</span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a>    dct <span class="ot">=</span> dcn <span class="sc">|&gt;</span> <span class="fu">filter</span>(topic <span class="sc">!=</span> x)</span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>    form <span class="ot">=</span> party_share <span class="sc">~</span> party_share_last <span class="sc">+</span> attention_others  </span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a>    form1 <span class="ot">=</span> <span class="fu">lm</span>(form,<span class="at">data =</span> dct)</span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a>    coeffs <span class="ot">=</span> <span class="fu">data.frame</span>(<span class="fu">summary</span>(form1)<span class="sc">$</span>coefficients[<span class="fu">c</span>(<span class="dv">2</span><span class="sc">:</span><span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)])</span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a>    coeffs<span class="sc">$</span>var <span class="ot">=</span> <span class="fu">rownames</span>(coeffs)</span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a>    coeffs<span class="sc">$</span>topic <span class="ot">=</span> x</span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a>    coeffs<span class="sc">$</span>cn <span class="ot">=</span> <span class="fu">unique</span>(dcn<span class="sc">$</span>cn)</span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a>    coeffs </span>
<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a>  }) })</span>
<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>res <span class="ot">=</span> <span class="fu">bind_rows</span>(res2)</span>
<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>res<span class="sc">$</span>cn2 <span class="ot">=</span> <span class="fu">factor</span>(res<span class="sc">$</span>cn)</span>
<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(res<span class="sc">$</span>cn2) <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;Belgium&quot;</span>,<span class="st">&quot;Germany&quot;</span>,<span class="st">&quot;Denmark&quot;</span>,<span class="st">&quot;France&quot;</span>,<span class="st">&quot;Italy&quot;</span>,<span class="st">&quot;Netherlands&quot;</span>,</span>
<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a>                    <span class="st">&quot;Sweden&quot;</span>,<span class="st">&quot;United Kingdom&quot;</span>)</span>
<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a>res<span class="sc">$</span>var2 <span class="ot">=</span> <span class="fu">case_when</span>(res<span class="sc">$</span>var <span class="sc">==</span> <span class="st">&quot;party_share_last&quot;</span> <span class="sc">~</span> <span class="st">&quot;Stability&quot;</span>,</span>
<span id="cb31-23"><a href="#cb31-23" aria-hidden="true" tabindex="-1"></a>                     res<span class="sc">$</span>var <span class="sc">==</span> <span class="st">&quot;attention_others&quot;</span> <span class="sc">~</span> <span class="st">&quot;Overlap&quot;</span>)</span>
<span id="cb31-24"><a href="#cb31-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-25"><a href="#cb31-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-26"><a href="#cb31-26" aria-hidden="true" tabindex="-1"></a><span class="co">#png(&quot;art/fig_notopic.png&quot;, width = 500, height = 600)</span></span>
<span id="cb31-27"><a href="#cb31-27" aria-hidden="true" tabindex="-1"></a>res <span class="sc">|&gt;</span> <span class="fu">filter</span>(topic <span class="sc">!=</span> <span class="fl">0.99</span>, cn <span class="sc">!=</span> <span class="st">&quot;sw&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb31-28"><a href="#cb31-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(topic,Estimate,<span class="at">color =</span> var2)) <span class="sc">+</span></span>
<span id="cb31-29"><a href="#cb31-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> Estimate <span class="sc">-</span> Std..Error, <span class="at">ymax =</span> Estimate <span class="sc">+</span> Std..Error)) <span class="sc">+</span></span>
<span id="cb31-30"><a href="#cb31-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span> <span class="fu">facet_wrap</span>(. <span class="sc">~</span> cn2,<span class="at">scales =</span> <span class="st">&quot;free&quot;</span>) <span class="sc">+</span> <span class="fu">coord_flip</span>() <span class="sc">+</span> </span>
<span id="cb31-31"><a href="#cb31-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;topic left out&quot;</span>, <span class="at">y=</span><span class="st">&quot;&quot;</span>,<span class="at">color =</span><span class="st">&quot;&quot;</span>) <span class="sc">+</span> </span>
<span id="cb31-32"><a href="#cb31-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<figure class="figure">
<p><img role="img" 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co">#dev.off()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="appendix-a16-media-attention-france-and-the-uk" class="level2">
<h2 class="anchored" data-anchor-id="appendix-a16-media-attention-france-and-the-uk">Appendix A16: Media attention France and the UK</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>fr1 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_3<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcf)</span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>fr2 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_6<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcf)</span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>fr3 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_9<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcf)</span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>uk1 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_3<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcu)</span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>uk2 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_6<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcu)</span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a>uk3 <span class="ot">=</span>  <span class="fu">feols</span>(party_share <span class="sc">~</span> (party_share_last)<span class="sc">+</span> (attention_others) <span class="sc">+</span> topic_9<span class="sc">|</span>year ,<span class="sc">~</span>party,</span>
<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a>             <span class="at">data =</span> dcu)</span>
<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>mods <span class="ot">=</span> <span class="fu">list</span>(fr1,fr2,fr3,uk1,uk2,uk3)</span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(mods) <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">rep</span>(<span class="fu">c</span>(<span class="st">&quot;FR:&quot;</span>,<span class="st">&quot;UK:&quot;</span>),<span class="at">each=</span><span class="dv">3</span>),<span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>))</span>
<span id="cb33-17"><a href="#cb33-17" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(mods,<span class="at">statistic =</span> <span class="cn">NULL</span>,</span>
<span id="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a>             <span class="at">estimate =</span> <span class="st">&quot;{estimate}({std.error}){stars}&quot;</span>,</span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a>             <span class="at">gof_omit =</span> <span class="st">&quot;IC|RMSE&quot;</span>,</span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a>             <span class="at">coef_rename =</span> <span class="fu">c</span>(<span class="st">&quot;Stability&quot;</span>,<span class="st">&quot;Overlap&quot;</span>,<span class="st">&quot;Media 3 months&quot;</span>,<span class="st">&quot;Media 6 months&quot;</span>,<span class="st">&quot;Media 9 months&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">FR:1</th>
                <th scope="col">FR:2</th>
                <th scope="col">FR:3</th>
                <th scope="col">UK:1</th>
                <th scope="col">UK:2</th>
                <th scope="col">UK:3</th>
              </tr>
        </thead>
        
        <tbody>
                <tr>
                  <td>Stability     </td>
                  <td>0.315(0.054)***</td>
                  <td>0.313(0.054)***</td>
                  <td>0.314(0.055)***</td>
                  <td>0.469(0.111)+ </td>
                  <td>0.469(0.111)+ </td>
                  <td>0.469(0.112)+ </td>
                </tr>
                <tr>
                  <td>Overlap       </td>
                  <td>0.491(0.093)***</td>
                  <td>0.490(0.091)***</td>
                  <td>0.489(0.089)***</td>
                  <td>0.382(0.019)**</td>
                  <td>0.385(0.018)**</td>
                  <td>0.383(0.018)**</td>
                </tr>
                <tr>
                  <td>Media 3 months</td>
                  <td>0.071(0.081)   </td>
                  <td>               </td>
                  <td>               </td>
                  <td>0.029(0.033)  </td>
                  <td>              </td>
                  <td>              </td>
                </tr>
                <tr>
                  <td>Media 6 months</td>
                  <td>               </td>
                  <td>0.076(0.078)   </td>
                  <td>               </td>
                  <td>              </td>
                  <td>0.023(0.029)  </td>
                  <td>              </td>
                </tr>
                <tr>
                  <td>Media 9 months</td>
                  <td>               </td>
                  <td>               </td>
                  <td>0.081(0.076)   </td>
                  <td>              </td>
                  <td>              </td>
                  <td>0.028(0.031)  </td>
                </tr>
                <tr>
                  <td>Num.Obs.      </td>
                  <td>450            </td>
                  <td>450            </td>
                  <td>450            </td>
                  <td>285           </td>
                  <td>285           </td>
                  <td>285           </td>
                </tr>
                <tr>
                  <td>R2            </td>
                  <td>0.384          </td>
                  <td>0.385          </td>
                  <td>0.385          </td>
                  <td>0.513         </td>
                  <td>0.512         </td>
                  <td>0.513         </td>
                </tr>
                <tr>
                  <td>R2 Adj.       </td>
                  <td>0.375          </td>
                  <td>0.375          </td>
                  <td>0.375          </td>
                  <td>0.501         </td>
                  <td>0.500         </td>
                  <td>0.501         </td>
                </tr>
                <tr>
                  <td>R2 Within     </td>
                  <td>0.383          </td>
                  <td>0.383          </td>
                  <td>0.384          </td>
                  <td>0.511         </td>
                  <td>0.510         </td>
                  <td>0.511         </td>
                </tr>
                <tr>
                  <td>R2 Within Adj.</td>
                  <td>0.379          </td>
                  <td>0.379          </td>
                  <td>0.380          </td>
                  <td>0.506         </td>
                  <td>0.505         </td>
                  <td>0.506         </td>
                </tr>
                <tr>
                  <td>Std.Errors    </td>
                  <td>by: party      </td>
                  <td>by: party      </td>
                  <td>by: party      </td>
                  <td>by: party     </td>
                  <td>by: party     </td>
                  <td>by: party     </td>
                </tr>
                <tr>
                  <td>FE: year      </td>
                  <td>X              </td>
                  <td>X              </td>
                  <td>X              </td>
                  <td>X             </td>
                  <td>X             </td>
                  <td>X             </td>
                </tr>
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</div>
</section>

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